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Journal articles on the topic 'Keypoints detection'
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1
Lu, Changsheng, and Piotr Koniusz. "Detect Any Keypoints: An Efficient Light-Weight Few-Shot Keypoint Detector." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (March 24, 2024): 3882–90. http://dx.doi.org/10.1609/aaai.v38i4.28180.
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Recently the prompt-based models have become popular across various language and vision tasks. Following that trend, we perform few-shot keypoint detection (FSKD) by detecting any keypoints in a query image, given the prompts formed by support images and keypoints. FSKD can be applied to detecting keypoints and poses of diverse animal species. In order to maintain flexibility of detecting varying number of keypoints, existing FSKD approaches modulate query feature map per support keypoint, then detect the corresponding keypoint from each modulated feature via a detection head. Such a separation of modulation-detection makes model heavy and slow when the number of keypoints increases. To overcome this issue, we design a novel light-weight detector which combines modulation and detection into one step, with the goal of reducing the computational cost without the drop of performance. Moreover, to bridge the large domain shift of keypoints between seen and unseen species, we further improve our model with mean feature based contrastive learning to align keypoint distributions, resulting in better keypoint representations for FSKD. Compared to the state of the art, our light-weight detector reduces the number of parameters by 50%, training/test time by 50%, and achieves 5.62% accuracy gain on 1-shot novel keypoint detection in the Animal pose dataset. Our model is also robust to the number of keypoints and saves memory when evaluating a large number of keypoints (e.g., 1000) per episode.
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Qian, Shenhan, Dongze Lian, Binqiang Zhao, Tong Liu, Bohui Zhu, Hai Li, and Shenghua Gao. "KGDet: Keypoint-Guided Fashion Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2449–57. http://dx.doi.org/10.1609/aaai.v35i3.16346.
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Locating and classifying clothes, usually referred to as clothing detection, is a fundamental task in fashion analysis. Motivated by the strong structural characteristics of clothes, we pursue a detection method enhanced by clothing keypoints, which is a compact and effective representation of structures. To incorporate the keypoint cues into clothing detection, we design a simple yet effective Keypoint-Guided clothing Detector, named KGDet. Such a detector can fully utilize information provided by keypoints with the following two aspects: i) integrating local features around keypoints to benefit both classification and regression; ii) generating accurate bounding boxes from keypoints. To effectively incorporate local features , two alternative modules are proposed. One is a multi-column keypoint-encoding-based feature aggregation module; the other is a keypoint-selection-based feature aggregation module. With either of the above modules as a bridge, a cascade strategy is introduced to refine detection performance progressively. Thanks to the keypoints, our KGDet obtains superior performance on the DeepFashion2 dataset and the FLD dataset with high efficiency.
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Li, L., L. Han, H. Cao, and M. Liu. "A SELF-SUPERVISED KEYPOINT DETECTION NETWORK FOR MULTIMODAL REMOTE SENSING IMAGES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 609–15. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-609-2022.
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Abstract. Currently, multimodal remote sensing images have complex geometric and radiometric distortions, which are beyond the reach of classical hand-crafted feature-based matching. Although keypoint matching methods have been developed in recent decades, most manual and deep learning-based techniques cannot effectively extract highly repeatable keypoints. To address that, we design a Siamese network with self-supervised training to generate similar keypoint feature maps between multimodal images, and detect highly repeatable keypoints by computing local spatial- and channel-domain peaks of the feature maps. We exploit the confidence level of keypoints to enable the detection network to evaluate potential keypoints with end-to-end trainability. Unlike most trainable detectors, it does not require the generation of pseudo-ground truth points. In the experiments, the proposed method is evaluated using various SAR and optical images covering different scenes. The results prove its superior keypoint detection performance compared with current state-of-art matching methods based on keypoints.
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Zhu, Z., W. Diao, K. Chen, L. Zhao, Z. Yan, W. Zhang, G. Xu, and X. Sun. "DIAMONDNET: SHIP DETECTION IN REMOTE SENSING IMAGES BY EXTRACTING AND CLUSTERING KEYPOINTS IN A DIAMOND." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 625–32. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-625-2020.
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Abstract. Ship detection plays an important role in military and civil fields. Despite it has been studied for decays, ship detection in remote sensing images is still a challenging topic. In this work, we come up with a novel ship detection framework based on the keypoint extraction technique. We use a convolutional neural network to detect ship keypoints and then cluster the keypoints into groups, where each group is composed of keypoints belonging to the same ship. The choice of the keypoints is specifically considered to derive an effective ship representation. One keypoint is located at the center of the ship and the rest four keypoints are located at the head, the tail, the midpoint of the left side and the midpoint of the right side, respectively. Since these keypoints are distributed in a diamond, we name our network DiamondNet. In addition, a corresponding clustering algorithm based on the geometric characteristics of the ships is proposed to cluster keypoints into groups. We demonstrate that our method provides a more flexible and effective way to represent ships than the popular anchor-based methods, since either the rectangular bounding box or the rotated bounding box of each ship instance can be easily derived from the ship keypoints. Experiments on two datasets reveal that our DiamondNet reaches the state-of-the-art results.
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Zhu, Juan, Zongwei Huang, Xiaofeng Yue, and Zeyuan Liu. "Point Cloud Registration Based on Local Variation of Surface Keypoints." Electronics 13, no. 1 (December 20, 2023): 35. http://dx.doi.org/10.3390/electronics13010035.
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Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.
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Mundt, Marion, Zachery Born, Molly Goldacre, and Jacqueline Alderson. "Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose." Sensors 23, no. 1 (December 21, 2022): 78. http://dx.doi.org/10.3390/s23010078.
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The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source pose estimation models (AlphaPose, BlazePose, and OpenPose) across varying movements, camera views, and trial lengths. Second, this study aimed to assess the suitability and interchangeability of keypoints detected by each pose estimation model when used as inputs into machine learning models for the estimation of GRFs. The keypoint detection rate of BlazePose was distinctly lower than that of AlphaPose and OpenPose. All pose estimation models achieved a high keypoint detection rate at the centre of an image frame and a lower detection rate in the true sagittal plane camera field of view, compared with slightly anteriorly or posteriorly located quasi-sagittal plane camera views. The three-dimensional ground reaction force, instantaneous loading rate, and peak force for running could be estimated using the keypoints of all three pose estimation models. However, only AlphaPose and OpenPose keypoints could be used interchangeably with a machine learning model trained to estimate GRFs based on AlphaPose keypoints resulting in a high estimation accuracy when OpenPose keypoints were used as inputs and vice versa. The findings of this study highlight the need for further evaluation of computer vision-based pose estimation models for application in biomechanical human modelling, and the limitations of machine learning-based GRF estimation models that rely on 2D keypoints. This is of particular relevance given that machine learning models informing athlete monitoring guidelines are being developed for application related to athlete well-being.
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Li, Zhen, Yuliang Gao, Qingqing Hong, Yuren Du, Seiichi Serikawa, and Lifeng Zhang. "Keypoint3D: Keypoint-Based and Anchor-Free 3D Object Detection for Autonomous Driving with Monocular Vision." Remote Sensing 15, no. 5 (February 22, 2023): 1210. http://dx.doi.org/10.3390/rs15051210.
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Autonomous driving has received enormous attention from the academic and industrial communities. However, achieving full driving autonomy is not a trivial task, because of the complex and dynamic driving environment. Perception ability is a tough challenge for autonomous driving, while 3D object detection serves as a breakthrough for providing precise and dependable 3D geometric information. Inspired by practical driving experiences of human experts, a pure visual scheme takes sufficient responsibility for safe and stable autonomous driving. In this paper, we proposed an anchor-free and keypoint-based 3D object detector with monocular vision, named Keypoint3D. We creatively leveraged 2D projected points from 3D objects‘ geometric centers as keypoints for object modeling. Additionally, for precise keypoints positioning, we utilized a novel self-adapting ellipse Gaussian filter (saEGF) on heatmaps, considering different objects’ shapes. We tried different variations of DLA-34 backbone and proposed a semi-aggregation DLA-34 (SADLA-34) network, which pruned the redundant aggregation branch but achieved better performance. Keypoint3D regressed the yaw angle in a Euclidean space,which resulted in a closed mathematical space avoiding singularities. Numerous experiments on the KITTI dataset for a moderate level have proven that Keypoint3D achieved the best speed-accuracy trade-off with an average precision of 39.1% at 18.9 FPS on 3D cars detection.
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Gu, Mingfei, Yinghua Wang, Hongwei Liu, and Penghui Wang. "PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector." Remote Sensing 14, no. 12 (June 17, 2022): 2900. http://dx.doi.org/10.3390/rs14122900.
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The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection.
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Paek, Kangho, Min Yao, Zhongwei Liu, and Hun Kim. "Log-Spiral Keypoint: A Robust Approach toward Image Patch Matching." Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/457495.
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Matching of keypoints across image patches forms the basis of computer vision applications, such as object detection, recognition, and tracking in real-world images. Most of keypoint methods are mainly used to match the high-resolution images, which always utilize an image pyramid for multiscale keypoint detection. In this paper, we propose a novel keypoint method to improve the matching performance of image patches with the low-resolution and small size. The location, scale, and orientation of keypoints are directly estimated from an original image patch using a Log-Spiral sampling pattern for keypoint detection without consideration of image pyramid. A Log-Spiral sampling pattern for keypoint description and two bit-generated functions are designed for generating a binary descriptor. Extensive experiments show that the proposed method is more effective and robust than existing binary-based methods for image patch matching.
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Li, Kecen, Haopeng Zhang, and Chenyu Hu. "Learning-Based Pose Estimation of Non-Cooperative Spacecrafts with Uncertainty Prediction." Aerospace 9, no. 10 (October 11, 2022): 592. http://dx.doi.org/10.3390/aerospace9100592.
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Estimation of spacecraft pose is essential for many space missions, such as formation flying, rendezvous, docking, repair, and space debris removal. We propose a learning-based method with uncertainty prediction to estimate the pose of a spacecraft from a monocular image. We first used a spacecraft detection network (SDN) to crop out the rectangular area in the original image where only spacecraft exist. A keypoint detection network (KDN) was then used to detect 11 pre-selected keypoints with obvious features from the cropped image and predict uncertainty. We propose a keypoints selection strategy to automatically select keypoints with higher detection accuracy from all detected keypoints. These selective keypoints were used to estimate the 6D pose of the spacecraft with the EPnP algorithm. We evaluated our method on the SPEED dataset. The experiments showed that our method outperforms heatmap-based and regression-based methods, and our effective uncertainty prediction can increase the final precision of the pose estimation.
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LI, JING, TAO YANG, QUAN PAN, YONG-MEI CHENG, and JUN HOU. "A NOVEL ALGORITHM FOR SPEEDING UP KEYPOINT DETECTION AND MATCHING." International Journal of Image and Graphics 08, no. 04 (October 2008): 643–61. http://dx.doi.org/10.1142/s0219467808003283.
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This work proposes a novel keypoint detector called QSIF (Quality and Spatial based Invariant Feature Detector). The primary contributions include: (1) a multilevel box filter is used to build the image scales efficiently and precisely, (2) by examining pixels in quality and spatial space simultaneously, QSIF can directly locate the keypoints without scale space extrema detection in the entire image spatial space, (3) QSIF can precisely control the number of output keypoints while maintaining almost the same repeatability of keypoint detection. This characteristic is essential in many real-time application fields. Extensive experimental results with images under scale, rotation, viewpoint and illumination changes demonstrate that the proposed QSIF has a stable and satisfied repeatability, and it can greatly speed up the keypoint detect and matching.
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Makarov, Aleksei, Marina Bolsunovskaya, and Olga Zhigunova. "Comparative analysis of methods for keypoint detection in images with different illumination level." MATEC Web of Conferences 239 (2018): 01028. http://dx.doi.org/10.1051/matecconf/201823901028.
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This article presents a comparative analysis of methods for keypoint detection that is a part of the research on the development of a surround camera system for large vehicles. Since the night time is the most dangerous for driving and the most difficult for image stitching, particular attention will be given to keypoint detection and image stitching in low light conditions. A comparative analysis of methods for keypoint detection has been made, a relevant technique has been developed and a series of experiments has been conducted to detect keypoints using the SURF, MSER, BRISK, Harris, FAST, and MinEigen methods. During the research, a search for identical keypoints for a pair of images, an analysis of their number and different methods of image stitching at different illumination levels were carried out. The results of the experiments are shown in graphs and tables.
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Wang, Yu, Xiaoye Wang, Zaiwang Gu, Weide Liu, Wee Siong Ng, Weimin Huang, and Jun Cheng. "SuperJunction: Learning-Based Junction Detection for Retinal Image Registration." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (March 24, 2024): 292–300. http://dx.doi.org/10.1609/aaai.v38i1.27782.
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Keypoints-based approaches have shown to be promising for retinal image registration, which superimpose two or more images from different views based on keypoint detection and description. However, existing approaches suffer from ineffective keypoint detector and descriptor training. Meanwhile, the non-linear mapping from 3D retinal structure to 2D images is often neglected. In this paper, we propose a novel learning-based junction detection approach for retinal image registration, which enhances both the keypoint detector and descriptor training. To improve the keypoint detection, it uses a multi-task vessel detection to regularize the model training, which helps to learn more representative features and reduce the risk of over-fitting. To achieve effective training for keypoints description, a new constrained negative sampling approach is proposed to compute the descriptor loss. Moreover, we also consider the non-linearity between retinal images from different views during matching. Experimental results on FIRE dataset show that our method achieves mean area under curve of 0.850, which is 12.6% higher than 0.755 by the state-of-the-art method. All the codes are available at https://github.com/samjcheng/SuperJunction.
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Wu, Yundong, Jiajia Liao, Yujun Liu, Kaiming Ding, Shimin Li, Zhilin Zhang, Guorong Cai, and Jinhe Su. "Knowledge-Driven Network for Object Detection." Algorithms 14, no. 7 (June 28, 2021): 195. http://dx.doi.org/10.3390/a14070195.
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Object detection is a challenging computer vision task with numerous real-world applications. In recent years, the concept of the object relationship model has become helpful for object detection and has been verified and realized in deep learning. Nonetheless, most approaches to modeling object relations are limited to using the anchor-based algorithms; they cannot be directly migrated to the anchor-free frameworks. The reason is that the anchor-free algorithms are used to eliminate the complex design of anchors and predict heatmaps to represent the locations of keypoints of different object categories, without considering the relationship between keypoints. Therefore, to better fuse the information between the heatmap channels, it is important to model the visual relationship between keypoints. In this paper, we present a knowledge-driven network (KDNet)—a new architecture that can aggregate and model keypoint relations to augment object features for detection. Specifically, it processes a set of keypoints simultaneously through interactions between their local and geometric features, thereby allowing the modeling of their relationship. Finally, the updated heatmaps were used to obtain the corners of the objects and determine their positions. The experimental results conducted on the RIDER dataset confirm the effectiveness of the proposed KDNet, which significantly outperformed other state-of-the-art object detection methods.
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Cheng, Yifan, Yang Li, Rentian Zhang, Zhiyong Gui, Chunwang Dong, and Rong Ma. "Locating Tea Bud Keypoints by Keypoint Detection Method Based on Convolutional Neural Network." Sustainability 15, no. 8 (April 19, 2023): 6898. http://dx.doi.org/10.3390/su15086898.
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Tea is one of the most consumed beverages in the whole world. Premium tea is a kind of tea with high nutrition, quality, and economic value. This study solves the problem of detecting premium tea buds in automatic plucking by training a modified Mask R-CNN network for tea bud detection in images. A new anchor generation method by adding additional anchors and the CIoU loss function were used in this modified model. In this study, the keypoint detection branch was optimized to locate tea bud keypoints, which, containing a fully convolutional network (FCN), is also built to locate the keypoints of bud objects. The built convolutional neural network was trained through our dataset and obtained an 86.6% precision and 88.3% recall for the bud object detection. The keypoint localization had a precision of 85.9% and a recall of 83.3%. In addition, a dataset for the tea buds and picking points was constructed in study. The experiments show that the developed model can be robust for a range of tea-bud-harvesting scenarios and introduces the possibility and theoretical basis for fully automated tea bud harvesting.
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Ghorbani, Fariborz, Hamid Ebadi, Norbert Pfeifer, and Amin Sedaghat. "Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure." Remote Sensing 14, no. 16 (August 21, 2022): 4099. http://dx.doi.org/10.3390/rs14164099.
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Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%.
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Vizilter, Y. V., V. S. Gorbatsevich, and A. S. Moiseenko. "Single-shot face and landmarks detector." Computer Optics 44, no. 4 (August 2020): 589–95. http://dx.doi.org/10.18287/2412-6179-co-674.
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Facial landmark detection is an important sub-task in solving a number of biometric facial recognition tasks. In face recognition systems, the construction of a biometric template occurs according to a previously aligned (normalized) face image and the normalization stage includes the task of finding facial keypoints. A balance between quality and speed of the facial keypoints detector is important in such a problem. This article proposes a CNN-based one-stage detector of faces and keypoints operating in real time and achieving high quality on a number of well-known test datasets (such as AFLW2000, COFW, Menpo2D). The proposed face and facial landmarks detector is based on the idea of a one-stage SSD object detector, which has established itself as an algorithm that provides high speed and high quality in object detection task. As a basic CNN architecture, we used the ShuffleNet V2 network. An important feature of the proposed algorithm is that the face and facial keypoint detection is done in one CNN forward pass, which can significantly save time at the implementation stage. Also, such multitasking allows one to reduce the percentage of errors in the facial keypoints detection task, which positively affects the final face recognition algorithm quality.
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Hamizan, Zaki, and Raden Sumiharto. "Sistem Pentautan Citra Udara Menggunakan Algoritme SURF dan Metode Reduksi Data." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 7, no. 2 (October 31, 2017): 127. http://dx.doi.org/10.22146/ijeis.18240.
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One of the algorithm for aerial image stitching system is SURF (Speeded Up Robust Features). It is a robust algorithm which is invariant to image scale, rotation, blurring, illumination, and affine transformation. Although SURF has good performance, some of the detected keypoints are not always considered as necessary keypoints . As a result, these unnecessary keypoints are needed to be eliminated to decrease computation time.The proposed system uses SURF detector in the detection process. The data reduction method will eliminate couple of keypoints which have near distance each other. Next, the keypoints will be described by SURF descriptor.The description Results further matched using FLANN. The next step is the search pattern with RANSAC homography matrix and stitch the picture to accumulate keypoints using warpPerpective.Stitching system are tested with some variations, such as scale variations, rotation variations, and overlap variations on the image. Based on the result, the proposed Data Reduction method has optimum value of minimal radius from 40 pixels to 100 pixels. The stitching process is still working with up to 90% keypoint number reduction. Average computation time using data reduction method are 39,41% faster than without data reduction method.
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Chen, Mu, Huaici Zhao, and Pengfei Liu. "Monocular 3D Object Detection Based on Uncertainty Prediction of Keypoints." Machines 10, no. 1 (December 26, 2021): 19. http://dx.doi.org/10.3390/machines10010019.
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Three-dimensional (3D) object detection is an important task in the field of machine vision, in which the detection of 3D objects using monocular vision is even more challenging. We observe that most of the existing monocular methods focus on the design of the feature extraction framework or embedded geometric constraints, but ignore the possible errors in the intermediate process of the detection pipeline. These errors may be further amplified in the subsequent processes. After exploring the existing detection framework of keypoints, we find that the accuracy of keypoints prediction will seriously affect the solution of 3D object position. Therefore, we propose a novel keypoints uncertainty prediction network (KUP-Net) for monocular 3D object detection. In this work, we design an uncertainty prediction module to characterize the uncertainty that exists in keypoint prediction. Then, the uncertainty is used for joint optimization with object position. In addition, we adopt position-encoding to assist the uncertainty prediction, and use a timing coefficient to optimize the learning process. The experiments on our detector are conducted on the KITTI benchmark. For the two levels of easy and moderate, we achieve accuracy of 17.26 and 11.78 in AP3D, and achieve accuracy of 23.59 and 16.63 in APBEV, which are higher than the latest method KM3D.
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Wang, Xiaofeng, Guanghui He, Chao Tang, Yali Han, and Shangping Wang. "Keypoints-Based Image Passive Forensics Method for Copy-Move Attacks." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 03 (February 22, 2016): 1655008. http://dx.doi.org/10.1142/s0218001416550089.
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A novel image passive forensics method for copy-move forgery detection is proposed. The proposed method combines block matching technology and feature point matching technology, and breaks away from the general framework of the visual feature-based approach that used local visual feature such as SIFT and followed by a clustering procedure to group feature points that are spatially close. In our work, image keypoints are extracted using Harris detector, and the statistical features of keypoint neighborhoods are used to generate forensics features. Then we proposed a new forensics features matching approach, in which, a region growth technology and a mismatch checking approach are developed to reduce mismatched keypoints and improve detected accuracy. We also develop a duplicate region detection method based on the distance frequency of corresponding keypoint pairs. The proposed method can detect duplicate regions for high resolution images. It has higher detection accuracy and computation efficiency. Experimental results show that the proposed method is robust for content-preserving manipulations such as JPEG compression, gamma adjustment, filtering, luminance enhancement, blurring, etc.
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Lee, Hong Gu, Min-Jee Kim, Su-bae Kim, Sujin Lee, Hoyoung Lee, Jeong Yong Sin, and Changyeun Mo. "Identifying an Image-Processing Method for Detection of Bee Mite in Honey Bee Based on Keypoint Analysis." Agriculture 13, no. 8 (July 28, 2023): 1511. http://dx.doi.org/10.3390/agriculture13081511.
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Economic and ecosystem issues associated with beekeeping may stem from bee mites rather than other bee diseases. The honey mites that stick to bees are small and possess a reddish-brown color, rendering it difficult to distinguish them with the naked eye. Objective and rapid technologies to detect bee mites are required. Image processing considerably improves detection performance. Therefore, this study proposes an image-processing method that can increase the detection performance of bee mites. A keypoint detection algorithm was implemented to identify keypoint location and frequencies in images of bees and bee mites. These parameters were analyzed to determine the rational measurement distance and image-processing. The change in the number of keypoints was analyzed by applying five-color model conversion, histogram normalization, and two-histogram equalization. The performance of the keypoints was verified by matching images with infested bees and mites. Among 30 given cases of image processing, the method applying normalization and equalization in the RGB color model image produced consistent quality data and was the most valid keypoint. Optimal image processing worked effectively in the measured 300 mm data in the range 300–1100 mm. The results of this study show that diverse image-processing techniques help to enhance the quality of bee mite detection significantly. This approach can be used in conjunction with an object detection deep-learning algorithm to monitor bee mites and diseases.
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Shi, Debo, Alireza Rahimpour, Amin Ghafourian, Mohammad Mahdi Naddaf Shargh, Devesh Upadhyay, Ty A. Lasky, and Iman Soltani. "Deep Bayesian-Assisted Keypoint Detection for Pose Estimation in Assembly Automation." Sensors 23, no. 13 (July 2, 2023): 6107. http://dx.doi.org/10.3390/s23136107.
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Pose estimation is crucial for automating assembly tasks, yet achieving sufficient accuracy for assembly automation remains challenging and part-specific. This paper presents a novel, streamlined approach to pose estimation that facilitates automation of assembly tasks. Our proposed method employs deep learning on a limited number of annotated images to identify a set of keypoints on the parts of interest. To compensate for network shortcomings and enhance accuracy we incorporated a Bayesian updating stage that leverages our detailed knowledge of the assembly part design. This Bayesian updating step refines the network output, significantly improving pose estimation accuracy. For this purpose, we utilized a subset of network-generated keypoint positions with higher quality as measurements, while for the remaining keypoints, the network outputs only serve as priors. The geometry data aid in constructing likelihood functions, which in turn result in enhanced posterior distributions of keypoint pixel positions. We then employed the maximum a posteriori (MAP) estimates of keypoint locations to obtain a final pose, allowing for an update to the nominal assembly trajectory. We evaluated our method on a 14-point snap-fit dash trim assembly for a Ford Mustang dashboard, demonstrating promising results. Our approach does not require tailoring to new applications, nor does it rely on extensive machine learning expertise or large amounts of training data. This makes our method a scalable and adaptable solution for the production floors.
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Zhou, Ling, Ruilin Wang, and Liyan Zhang. "Accurate Robot Arm Attitude Estimation Based on Multi-View Images and Super-Resolution Keypoint Detection Networks." Sensors 24, no. 1 (January 4, 2024): 305. http://dx.doi.org/10.3390/s24010305.
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Robot arm monitoring is often required in intelligent industrial scenarios. A two-stage method for robot arm attitude estimation based on multi-view images is proposed. In the first stage, a super-resolution keypoint detection network (SRKDNet) is proposed. The SRKDNet incorporates a subpixel convolution module in the backbone neural network, which can output high-resolution heatmaps for keypoint detection without significantly increasing the computational resource consumption. Efficient virtual and real sampling and SRKDNet training methods are put forward. The SRKDNet is trained with generated virtual data and fine-tuned with real sample data. This method decreases the time and manpower consumed in collecting data in real scenarios and achieves a better generalization effect on real data. A coarse-to-fine dual-SRKDNet detection mechanism is proposed and verified. Full-view and close-up dual SRKDNets are executed to first detect the keypoints and then refine the results. The keypoint detection accuracy, PCK@0.15, for the real robot arm reaches up to 96.07%. In the second stage, an equation system, involving the camera imaging model, the robot arm kinematic model and keypoints with different confidence values, is established to solve the unknown rotation angles of the joints. The proposed confidence-based keypoint screening scheme makes full use of the information redundancy of multi-view images to ensure attitude estimation accuracy. Experiments on a real UR10 robot arm under three views demonstrate that the average estimation error of the joint angles is 0.53 degrees, which is superior to that achieved with the comparison methods.
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Yang, Lian, and Zhangping Lu. "A New Scheme for Keypoint Detection and Description." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/310704.
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The keypoint detection and its description are two critical aspects of local keypoints matching which is vital in some computer vision and pattern recognition applications. This paper presents a new scale-invariant and rotation-invariant detector and descriptor, coined, respectively, DDoG and FBRK. At first the Hilbert curve scanning is applied to converting a two-dimensional (2D) digital image into a one-dimensional (1D) gray-level sequence. Then, based on the 1D image sequence, an approximation of DoG detector using second-order difference-of-Gaussian function is proposed. Finally, a new fast binary ratio-based keypoint descriptor is proposed. That is achieved by using the ratio-relationships of the keypoint pixel value with other pixel of values around the keypoint in scale space. Experimental results show that the proposed methods can be computed much faster and approximate or even outperform the existing methods with respect to performance.
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Ahmad, Niaz, Jawad Khan, Jeremy Yuhyun Kim, and Youngmoon Lee. "Joint Human Pose Estimation and Instance Segmentation with PosePlusSeg." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 69–76. http://dx.doi.org/10.1609/aaai.v36i1.19880.
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Despite the advances in multi-person pose estimation, state-of-the-art techniques only deliver the human pose structure.Yet, they do not leverage the keypoints of human pose to deliver whole-body shape information for human instance segmentation. This paper presents PosePlusSeg, a joint model designed for both human pose estimation and instance segmentation. For pose estimation, PosePlusSeg first takes a bottom-up approach to detect the soft and hard keypoints of individuals by producing a strong keypoint heat map, then improves the keypoint detection confidence score by producing a body heat map. For instance segmentation, PosePlusSeg generates a mask offset where keypoint is defined as a centroid for the pixels in the embedding space, enabling instance-level segmentation for the human class. Finally, we propose a new pose and instance segmentation algorithm that enables PosePlusSeg to determine the joint structure of the human pose and instance segmentation. Experiments using the COCO challenging dataset demonstrate that PosePlusSeg copes better with challenging scenarios, like occlusions, en-tangled limbs, and overlapped people. PosePlusSeg outperforms state-of-the-art detection-based approaches achieving a 0.728 mAP for human pose estimation and a 0.445 mAP for instance segmentation. Code has been made available at: https://github.com/RaiseLab/PosePlusSeg.
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Song, Fei, Ruofei Ma, Tao Lei, and Zhenming Peng. "RAIH-Det: An End-to-End Rotated Aircraft and Aircraft Head Detector Based on ConvNeXt and Cyclical Focal Loss in Optical Remote Sensing Images." Remote Sensing 15, no. 9 (April 29, 2023): 2364. http://dx.doi.org/10.3390/rs15092364.
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In airport ground-traffic surveillance systems, the detection of an aircraft and its head (AIH) is an important task in aircraft trajectory judgment. However, accurately detecting an AIH in high-resolution optical remote sensing images is a challenging task due to the difficulty in effectively modeling the features of aircraft objects, such as changes in appearance, large-scale differences, complex compositions, and cluttered background. In this paper, we propose an end-to-end rotated aircraft and aircraft head detector (RAIH-Det) based on ConvNeXt-T (Tiny) and cyclical local loss. Firstly, a new U-shaped network based on ConvNeXt-T with the same performance as the Local Vision Transformer (e.g., Swin Transformer) is presented to assess the relationships among aircraft in the spatial domain. Then, in order to enhance the sharing of more mutual information, the extended BBAVectors with six vectors captures the oriented bounding box (OBB) of the aircraft in any direction, which can assist in head keypoint detection by exploiting the relationship between the local and overall structural information of aircraft. Simultaneously, variant cyclical focal loss is adopted to regress the heatmap location of keypoints on the aircraft head to focus on more reliable samples. Furthermore, to perform a study on AIH detection and simplify aircraft head detection, the OBBs of the “plane” category in the DOTA-v1.5 dataset and the corresponding head keypoints annotated by our volunteers were integrated into a new dataset called DOTA-Plane. Compared with other state-of-the-art rotated object and keypoint detectors, RAIH-Det, as evaluated on DOTA-Plane, offered superior performance.
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Gonçalves, Tiago, Wilson Silva, Maria J. Cardoso, and Jaime S. Cardoso. "Deep Image Segmentation for Breast Keypoint Detection." Proceedings 54, no. 1 (August 21, 2020): 35. http://dx.doi.org/10.3390/proceedings2020054035.
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The main aim of breast cancer conservative treatment is the optimisation of the aesthetic outcome and, implicitly, women’s quality of life, without jeopardising local cancer control and overall survival. Moreover, there has been an effort to try to define an optimal tool capable of performing the aesthetic evaluation of breast cancer conservative treatment outcomes. Recently, a deep learning algorithm that integrates the learning of keypoints’ probability maps in the loss function as a regularisation term for the robust learning of the keypoint localisation has been proposed. However, it achieves the best results when used in cooperation with a shortest-path algorithm that models images as graphs. In this work, we analysed a novel algorithm based on the interaction of deep image segmentation and deep keypoint detection models capable of improving both state-of-the-art performance and execution-time on the breast keypoint detection task.
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Utla, C. S., A. Dashora, L. Chandrasekhar Reddy, and A. V. Kulkarni. "ANALYSIS OF GROUND SAMPLING DISTANCE OF CONVERGENT IMAGES FOR KEYPOINTS DETECTION FOR CLOSE-RANGE PHOTOGRAMMETRY." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 93–98. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-93-2022.
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Abstract. Keypoint detection for image matching is an important step in close range photogrammetry. It essentially depends upon ground sampling distance (GSD) of an image. For a convergent image, GSD variations on the either side of image axis are not equal. Moreover, GSD also varies according to camera position placed at constant distance from an object. This paper investigates and analyses the GSDs of convergent images for various geometrical configurations of camera positions on a circular arc for a building corner. GSD expressions and rates of GSD change are derived for left and right edges of a convergent image. Both GSD and rate of GSD change are characterized by non-linear mathematical functions of camera FOV, and its position on circular for a given corner. Experiments are conducted to acquire varying number of convergent images on circular arcs of different radius. Keypoints for convergent images are influenced more by rate of GSD change than the GSD. The study determines a critical value of 28 for rate of GSD change. The correct matching of keypoints in two images is limited within FOVs corresponding to the critical value. An example demonstrating the correct keypoint matching for two images is presented.
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Drążkowska, Marta. "Detection of Pediatric Femur Configuration on X-ray Images." Applied Sciences 11, no. 20 (October 14, 2021): 9538. http://dx.doi.org/10.3390/app11209538.
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In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images from the publicly available LERA dataset.
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Drążkowska, Marta. "Detection of Pediatric Femur Configuration on X-ray Images." Applied Sciences 11, no. 20 (October 14, 2021): 9538. http://dx.doi.org/10.3390/app11209538.
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In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images from the publicly available LERA dataset.
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Li, Leyang, Guixing Cao, Jun Liu, and Xiaohao Cai. "Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites." Sensors 23, no. 23 (November 28, 2023): 9479. http://dx.doi.org/10.3390/s23239479.
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The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance. To address this issue, we propose a method that could be implemented well on resource-limited satellites for remote sensing images ship matching by leveraging line features. A keypoint extraction strategy called line feature based keypoint detection (LFKD) is designed using line features to choose and filter keypoints. It can strengthen the features at corners and edges of objects and also can significantly reduce the number of keypoints that cause false matches. We also present an end-to-end matching process dependent on a new crop patching function, which helps to reduce complexity. The matching accuracy achieved by the proposed method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to the state-of-the-art methods in remote sensing scenes in extensive experiments, our keypoint extraction method can be combined with all existing CNN models that can obtain descriptors, and also improve the matching accuracy. The results show that our method can achieve ∼50% test speed boost and ∼30% memory saving in our created dataset and public datasets.
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Li, Liangzhi, Ling Han, and Yuanxin Ye. "Self-Supervised Keypoint Detection and Cross-Fusion Matching Networks for Multimodal Remote Sensing Image Registration." Remote Sensing 14, no. 15 (July 27, 2022): 3599. http://dx.doi.org/10.3390/rs14153599.
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Remote sensing image matching is the basis upon which to obtain integrated observations and complementary information representation of the same scene from multiple source sensors, which is a prerequisite for remote sensing tasks such as remote sensing image fusion and change detection. However, the intricate geometric and radiometric differences between the multimodal images render the registration quite challenging. Although multimodal remote sensing image matching methods have been developed in recent decades, most classical and deep learning based techniques cannot effectively extract high repeatable keypoints and discriminative descriptors for multimodal images. Therefore, we propose a two-step “detection + matching” framework in this paper, where each step consists of a deep neural network. A self-supervised detection network is first designed to generate similar keypoint feature maps between multimodal images, which is used to detect highly repeatable keypoints. We then propose a cross-fusion matching network, which aims to exploit global optimization and fusion information for cross-modal feature descriptors and matching. The experiments show that the proposed method has superior feature detection and matching performance compared with current state-of-the-art methods. Specifically, the keypoint repetition rate of the detection network and the NN mAP of the matching network are 0.435 and 0.712 on test datasets, respectively. The proposed whole pipeline framework is evaluated, which achieves an average M.S. and RMSE of 0.298 and 3.41, respectively. This provides a novel solution for the joint use of multimodal remote sensing images for observation and localization.
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Kumar, Suganthi, and Rajkumar Soundrapandiyan. "multiple secret image embedding in dynamic ROI keypoints based on hybrid Speeded Up Scale Invariant Robust Features (h-SUSIRF) algorithm." ELCVIA Electronic Letters on Computer Vision and Image Analysis 21, no. 1 (July 19, 2022): 78–100. http://dx.doi.org/10.5565/rev/elcvia.1470.
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This paper presents a robust and high-capacity video steganography framework using a hybrid Speeded Up Scale Invariant Robust Features (h-SUSIRF) keypoints detection algorithm. There are two main objectives in this method: (1) determining the dynamic Region of Interest (ROI) keypoints in video scenes and (2) embedding the appropriate secret data into the identified regions. In this work, the h-SUSIRF keypoints detection scheme is proposed to find keypoints within the scenes. These identified keypoints are dilated to form the dynamic ROI keypoints. Finally, the secret images are embedded into the dynamic ROI keypoints’ locations of the scenes using the substitution method. The performance of the proposed method (PM) is evaluated using standard metrics Structural Similarity Index Measure (SSIM), Capacity (Cp), and Bit Error Rate (BER). The standard of the video is ensured by Video Quality Measure (VQM). To examine the efficacy of the PM some recent steganalysis schemes are applied to calculate the detection ratio and the Receiver Operating Characteristics (ROC) curve is analyzed. From the experimental analysis, it is deduced that the PM surpasses the contemporary methods by achieving significant results in terms of imperceptibility, capacity, robustness with lower computational complexity.
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Zhu, Qinping, and Zhichun Mu. "Local and Holistic Feature Fusion for Occlusion-Robust 3D Ear Recognition." Symmetry 10, no. 11 (November 1, 2018): 565. http://dx.doi.org/10.3390/sym10110565.
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Occlusion over ear surfaces results in performance degradation of ear registration and recognition systems. In this paper, we propose an occlusion-resistant three-dimensional (3D) ear recognition system consisting of four primary components: (1) an ear detection component, (2) a local feature extraction and matching component, (3) a holistic matching component, and (4) a decision-level fusion algorithm. The ear detection component is implemented based on faster region-based convolutional neural networks. In the local feature extraction and matching component, a symmetric space-centered 3D shape descriptor based on the surface patch histogram of indexed shapes (SPHIS) is used to generate a set of keypoints and a feature vector for each keypoint. Then, a two-step noncooperative game theory (NGT)-based method is proposed. The proposed symmetric game-based method is effectively applied to determine a set of keypoints that satisfy the rigid constraints from initial keypoint correspondences. In the holistic matching component, a proposed variant of breed surface voxelization is used to calculate the holistic registration error. Finally, the decision-level fusion algorithm is applied to generate the final match scores. Evaluation results from experiments conducted show that the proposed method produces competitive results for partial occlusion on a dataset consisting of natural and random occlusion.
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Romero-González, Cristina, Ismael García-Varea, and Jesus Martínez-Gómez. "Shape binary patterns: an efficient local descriptor and keypoint detector for point clouds." Multimedia Tools and Applications 81, no. 3 (January 2022): 3577–601. http://dx.doi.org/10.1007/s11042-021-11586-5.
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AbstractMany of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational capabilities available. In autonomous robots, these capabilities are usually limited and, consequently, images cannot be processed adequately. For this reason, some robot vision tasks still benefit from a more classic approach based on keypoint detectors and local descriptors. In 2D images, the use of binary representations for visual tasks has shown that, with lower computational requirements, they can obtain a performance comparable to classic real-value techniques. However, these achievements have not been fully translated to 3D images, where research is mainly focused on real-value approaches. Thus, in this paper, we propose a keypoint detector and local descriptor based on 3D binary patterns. The experimentation demonstrates that our proposal is competitive against state-of-the-art techniques, while its processing can be performed more efficiently.
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Xu, Junjie, Bin Song, Xi Yang, and Xiaoting Nan. "An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation." Remote Sensing 12, no. 23 (November 25, 2020): 3857. http://dx.doi.org/10.3390/rs12233857.
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The on-board pose estimation of uncooperative target is an essential ability for close-proximity formation flying missions, on-orbit servicing, active debris removal and space exploration. However, the main issues of this research are: first, traditional pose determination algorithms result in a semantic gap and poor generalization abilities. Second, specific pose information cannot be accurately known in a complicated space target imaging environment. Deep learning methods can effectively solve these problems; thus, we propose a pose estimation algorithm that is based on deep learning. We use keypoints detection method to estimate the pose of space targets. For complicated space target imaging environment, we combined the high-resolution network with dilated convolution and online hard keypoint mining strategy. The improved network pays more attention to the obscured keypoints, has a larger receptive field, and improves the detection accuracy. Extensive experiments have been conducted and the results demonstrate that the proposed algorithms can effectively reduce the error rate of pose estimation and, compared with the related pose estimation methods, our proposed model has a higher detection accuracy and a lower pose determination error rate in the speed dataset.
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Volkmann, Nina, Claudius Zelenka, Archana Malavalli Devaraju, Johannes Brünger, Jenny Stracke, Birgit Spindler, Nicole Kemper, and Reinhard Koch. "Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks." Sensors 22, no. 14 (July 11, 2022): 5188. http://dx.doi.org/10.3390/s22145188.
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Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.
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Gajic, Dusan, Gorana Gojic, Dinu Dragan, and Veljko Petrovic. "Comparative evaluation of keypoint detectors for 3d digital avatar reconstruction." Facta universitatis - series: Electronics and Energetics 33, no. 3 (2020): 379–94. http://dx.doi.org/10.2298/fuee2003379g.
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Three-dimensional personalized human avatars have been successfully utilized in shopping, entertainment, education, and health applications. However, it is still a challenging task to obtain both a complete and highly detailed avatar automatically. One approach is to use general-purpose, photogrammetry-based algorithms on a series of overlapping images of the person. We argue that the quality of avatar reconstruction can be increased by modifying parts of the photogrammetry-based algorithm pipeline to be more specifically tailored to the human body shape. In this context, we perform an extensive, standalone evaluation of eleven algorithms for keypoint detection, which is the first phase of the photogrammetry-based reconstruction pipeline. We include well established, patented Distinctive image features from scale-invariant keypoints (SIFT) and Speeded up robust features (SURF) detection algorithms as a baseline since they are widely incorporated into photogrammetry-based software. All experiments are conducted on a dataset of 378 images of human body captured in a controlled, multi-view stereo setup. Our findings are that binary detectors highly outperform commonly used SIFT-like detectors in the avatar reconstruction task, both in terms of detection speed and in number of detected keypoints.
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Liu, Ruiqing, Juncai Zhu, and Xiaoping Rao. "Murine Motion Behavior Recognition Based on DeepLabCut and Convolutional Long Short-Term Memory Network." Symmetry 14, no. 7 (June 29, 2022): 1340. http://dx.doi.org/10.3390/sym14071340.
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Murine behavior recognition is widely used in biology, neuroscience, pharmacology, and other aspects of research, and provides a basis for judging the psychological and physiological state of mice. To solve the problem whereby traditional behavior recognition methods only model behavioral changes in mice over time or space, we propose a symmetrical algorithm that can capture spatiotemporal information based on behavioral changes. The algorithm first uses the improved DeepLabCut keypoint detection algorithm to locate the nose, left ear, right ear, and tail root of the mouse, and then uses the ConvLSTM network to extract spatiotemporal information from the keypoint feature map sequence to classify five behaviors of mice: walking straight, resting, grooming, standing upright, and turning. We developed a murine keypoint detection and behavior recognition dataset, and experiments showed that the method achieved a percentage of correct keypoints (PCK) of 87±1% at three scales and against four backgrounds, while the classification accuracy for the five kinds of behaviors reached 93±1%. The proposed method is thus accurate for keypoint detection and behavior recognition, and is a useful tool for murine motion behavior recognition.
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Park, Jun Young, Tae An Kang, Yong Ho Moon, and Il Kyu Eom. "Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram." Symmetry 12, no. 4 (March 26, 2020): 492. http://dx.doi.org/10.3390/sym12040492.
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Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.
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Li, Bangjie, Dongdong Guan, Xiaolong Zheng, Zhengsheng Chen, and Lefei Pan. "SD-CapsNet: A Siamese Dense Capsule Network for SAR Image Registration with Complex Scenes." Remote Sensing 15, no. 7 (March 31, 2023): 1871. http://dx.doi.org/10.3390/rs15071871.
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SAR image registration is the basis for applications such as change detection, image fusion, and three-dimensional reconstruction. Although CNN-based SAR image registration methods have achieved competitive results, they are insensitive to small displacement errors in matched point pairs and do not provide a comprehensive description of keypoint information in complex scenes. In addition, existing keypoint detectors are unable to obtain a uniform distribution of keypoints in SAR images with complex scenes. In this paper, we propose a texture constraint-based phase congruency (TCPC) keypoint detector that uses a rotation-invariant local binary pattern operator (RI-LBP) to remove keypoints that may be located at overlay or shadow locations. Then, we propose a Siamese dense capsule network (SD-CapsNet) to extract more accurate feature descriptors. Then, we define and verify that the feature descriptors in capsule form contain intensity, texture, orientation, and structure information that is useful for SAR image registration. In addition, we define a novel distance metric for the feature descriptors in capsule form and feed it into the Hard L2 loss function for model training. Experimental results for six pairs of SAR images demonstrate that, compared to other state-of-the-art methods, our proposed method achieves more robust results in complex scenes, with the number of correctly matched keypoint pairs (NCM) at least 2 to 3 times higher than the comparison methods, a root mean square error (RMSE) at most 0.27 lower than the compared methods.
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Assi, R., T. Landes, A. Murtiyoso, and P. Grussenmeyer. "ASSESSMENT OF A KEYPOINTS DETECTOR FOR THE REGISTRATION OF INDOOR AND OUTDOOR HERITAGE POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 20, 2019): 133–38. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-133-2019.
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<p><strong>Abstract.</strong> In the context of architectural heritage preservation, constructing building information models is an important task. However, conceiving a pertinent model is a difficult, time consuming and user-dependent task. Our laboratory has been researching methods to decrease the time and errors inferred by manual segmentation of point clouds. In the perspective of automatization of the process, we implemented an automated registration method that used only keypoints. Keypoints are special points that hold more information about the global structure of the cloud. In order to detect keypoints, we used the Point Cloud Library (PCL) toolbox. The pertinence of the method was evaluated by registering more than 300 clouds of the zoological museum of Strasbourg. The quality of the keypoint detection was first verified on geo-referenced indoor point clouds. Then we applied this method to register the indoor and outdoor point clouds that have much less area in common; those common points being generally the doors and windows of the façade. The registrations of indoor point clouds were satisfying, with mean distances to the ground truth inferior to 20&thinsp;cm. While the first result for joint indoor/outdoor registration are promising, it may be improved in future works.</p>
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Ren, Xiaoyuan, Libing Jiang, Xiaoan Tang, and Weichun Liu. "3D Wireframe Modeling and Viewpoint Estimation for Multi-Class Objects Combining Deep Neural Network and Deformable Model Matching." Applied Sciences 9, no. 10 (May 14, 2019): 1975. http://dx.doi.org/10.3390/app9101975.
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The accuracy of 3D viewpoint and shape estimation from 2D images has been greatly improved by machine learning, especially deep learning technology such as the convolution neural network (CNN). However, current methods are always valid only for one specific category and have exhibited poor performance when generalized to other categories, which means that multiple detectors or networks are needed for multi-class object image cases. In this paper, we propose a method with strong generalization ability, which incorporates only one CNN with deformable model matching processing for the 3D viewpoint and the shape estimation of multi-class object image cases. The CNN is utilized to detect keypoints of the potential object from the image, while a deformable model matching stage is designed to conduct 3D wireframe modeling and viewpoint estimation simultaneously with the support of the detected keypoints. Besides, parameter estimation by deformable model matching processing has robust fault-tolerance to the keypoint detection results containing mistaken keypoints. The proposed method is evaluated on Pascal3D+ dataset. Experiments show that the proposed method performs well in both parameter estimation accuracy and the multi-class objects generalization. This research is a useful exploration to extend the generalization of deep learning in specific tasks.
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Jin, Ren, Jiaqi Jiang, Yuhua Qi, Defu Lin, and Tao Song. "Drone Detection and Pose Estimation Using Relational Graph Networks." Sensors 19, no. 6 (March 26, 2019): 1479. http://dx.doi.org/10.3390/s19061479.
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With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm.
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Nguyen, Hung-Cuong, Thi-Hao Nguyen, Jakub Nowak, Aleksander Byrski, Agnieszka Siwocha, and Van-Hung Le. "Combined YOLOv5 and HRNet for High Accuracy 2D Keypoint and Human Pose Estimation." Journal of Artificial Intelligence and Soft Computing Research 12, no. 4 (October 1, 2022): 281–98. http://dx.doi.org/10.2478/jaiscr-2022-0019.
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Abstract Two-dimensional human pose estimation has been widely applied in real-world applications such as sports analysis, medical fall detection, human-robot interaction, with many positive results obtained utilizing Convolutional Neural Networks (CNNs). Li et al. at CVPR 2020 proposed a study in which they achieved high accuracy in estimating 2D keypoints estimation/2D human pose estimation. However, the study performed estimation only on the cropped human image data. In this research, we propose a method for automatically detecting and estimating human poses in photos using a combination of YOLOv5 + CC (Contextual Constraints) and HRNet. Our approach inherits the speed of the YOLOv5 for detecting humans and the efficiency of the HRNet for estimating 2D keypoints/2D human pose on the images. We also performed human marking on the images by bounding boxes of the Human 3.6M dataset (Protocol #1) for human detection evaluation. Our approach obtained high detection results in the image and the processing time is 55 FPS on the Human 3.6M dataset (Protocol #1). The mean error distance is 5.14 pixels on the full size of the image (1000 × 1002). In particular, the average results of 2D human pose estimation/2D keypoints estimation are 94.8% of PCK and 99.2% of PDJ@0.4 (head joint). The results are available.
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Jiao, Runzhi, Qingsong Wang, Tao Lai, and Haifeng Huang. "Multi-Hypothesis Topological Isomorphism Matching Method for Synthetic Aperture Radar Images with Large Geometric Distortion." Remote Sensing 13, no. 22 (November 17, 2021): 4637. http://dx.doi.org/10.3390/rs13224637.
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The dramatic undulations of a mountainous terrain will introduce large geometric distortions in each Synthetic Aperture Radar (SAR) image with different look angles, resulting in a poor registration performance. To this end, this paper proposes a multi-hypothesis topological isomorphism matching method for SAR images with large geometric distortions. The method includes the Ridge-Line Keypoint Detection (RLKD) and Multi-Hypothesis Topological Isomorphism Matching (MHTIM). Firstly, based on the analysis of the ridge structure, a ridge keypoint detection module and a keypoint similarity description method are designed, which aim to quickly produce a small number of stable matching keypoint pairs under large look angle differences and large terrain undulations. The keypoint pairs are further fed into the MHTIM module. Subsequently, the MHTIM method is proposed, which uses the stability and isomorphism of the topological structure of the keypoint set under different perspectives to generate a variety of matching hypotheses, and iteratively achieves the keypoint matching. This method uses both local and global geometric relationships between two keypoints, hence it achieving better performance compared with traditional methods. We tested our approach on both simulated and real mountain SAR images with different look angles and different elevation ranges. The experimental results demonstrate the effectiveness and stable matching performance of our approach.
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Fu, Dengyu, and Wei Wu. "High-Resolution Representation Learning for Human Pose Estimation based on Transformer." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2189/1/012023.
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Abstract Human pose estimation requires accurate coordinate values for the prediction of human joints, which requires a high-resolution representation to effectively improve accuracy. For some difficult joint prediction tasks, it is not only necessary to look at the characteristics of the joint points themselves, but also to make judgments in combination with the context of the whole image. Generally, the resolution will be reduced when the context information is obtained. In this process, it will inevitably lose some spatial information and make the prediction inaccurate. In this paper, we propose a high-resolution human pose estimation network based on Transformer to reduce the impact of spatial information loss on keypoints estimation. In detail, we use low-level convolution neural network to extract low-level semantics from the image, and then the Transformer is used to capture the image context to further predict the key points of the human body, obtain the high-resolution representation. The experiments show that our network can accurately predict the positions of keypoints, we achieve state-of-art results on the COCO keypoint detection dataset.
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Zhang, Jiaming, Xuejuan Hu, Tan Zhang, Shiqian Liu, Kai Hu, Ting He, Xiaokun Yang, et al. "Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection." Electronics 12, no. 6 (March 17, 2023): 1435. http://dx.doi.org/10.3390/electronics12061435.
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Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypoints in the image, which solves the problem of inter-period matching of keypoints. The brightness description introduces the concept of bright and dark points, which improves the distinguishability of BNCD and reduces the calculation amount of matching. Experimental results show that in circuit board image registration, the matching precision rate and recall rate of BNCD is better than that of classic algorithms such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF), and the calculation of descriptors takes less time.
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Mishra, Parul, Nishchol Mishra, Sanjeev Sharma, and Ravindra Patel. "Region Duplication Forgery Detection Technique Based on SURF and HAC." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/267691.
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Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.
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Chang, Leonardo, José Hernández-Palancar, L. Enrique Sucar, and Miguel Arias-Estrada. "FPGA-based detection of SIFT interest keypoints." Machine Vision and Applications 24, no. 2 (May 30, 2012): 371–92. http://dx.doi.org/10.1007/s00138-012-0430-8.
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