Relevant bibliographies by topics / Keypoints detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Keypoints detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Keypoints detection"

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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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Dissertations / Theses on the topic "Keypoints detection"

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Gale, Timothy Edward. "Improved detection and quantisation of keypoints in the complex wavelet domain." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277713.

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An algorithm which is able to consistently identify features in an image is a basic building block of many object recognition systems. Attaining sufficient consistency is challenging, because factors such as pose and lighting can dramatically change a feature’s appearance. Effective feature identification therefore requires both a reliable and accurate keypoint detector and a discriminative categoriser (or quantiser). The Dual Tree Complex Wavelet Transform (DTCWT) decomposes an image into oriented subbands at a range of scales. The resulting domain is arguably well suited for further image analysis tasks such as feature identification. This thesis develops feature identification in the complex wavelet domain, building on previous keypoint detection work and exploring the use of random forests for descriptor quantisation. Firstly, we extended earlier work on keypoint detection energy functions. Existing complex wavelet based detectors were observed to suffer from two defects: a tendency to produce keypoints on straight edges at particular orientations and sensitivity to small translations of the image. We introduced a new corner energy function based on the Same Level Product (SLP) transform. This function performed well compared to previous ones, combining competitive edge rejection and positional stability properties. Secondly, we investigated the effect of changing the resolution at which the energy function is sampled. We used the undecimated DTCWT to calculate energy maps at the same resolution as the original images. This revealed the presence of fine details which could not be accurately interpolated from an energy map at the standard resolution. As a result, doubling the resolution of the map along each axis significantly improved both the reliability and posi-tional accuracy of detections. However, calculating the map using interpolated coefficients resulted in artefacts introduced by inaccuracies in the interpolation. We therefore proposed a modification to the standard DTCWT structure which doubles its output resolution for a modest computational cost. Thirdly, we developed a random forest based quantiser which operates on complex wavelet polar matching descriptors, with optional rotational invariance. Trees were evaluated on the basis of how consistently they quantised features into the same bins, and several examples of each feature were obtained by means of tracking. We found that the trees produced the most consistent quantisations when they were trained with a second set of tracked keypoints. Detecting keypoints using the the higher resolution energy maps also resulted in more consistent quantiser outputs, indicating the importance of the choice of detector on quantiser performance. Finally, we introduced a fast implementation of the DTCWT, keypoint detection and descriptor extraction algorithms for OpenCL-capable GPUs. Several aspects were optimised to enable it to run more efficiently on modern hardware, allowing it to process HD footage in faster than real time. This particularly aided the development of the detector algorithms by permitting interactive exploration of their failure modes using a live camera feed.
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Avigni, Andrea. "Learning to detect good image features." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12856/.

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State-of-the-art keypoint detection algorithms have been designed to extract specific structures from images and to achieve a high keypoint repeatability, which means that they should find the same points in images undergoing specific transformations. However, this criterion does not guarantee that the selected keypoints will be the optimal ones during the successive matching step. The approach that has been developed in this thesis work is aimed at extracting keypoints that maximize the matching performance according to a pre-selected image descriptor. In order to do that, a classifier has been trained on a set of “good” and “bad” descriptors extracted from training images that are affected by a set of pre-defined nuisances. The set of “good” keypoints used for the training is filled with those vectors that are related to the points that gave correct matches during an initial matching step. On the contrary, randomly chosen points that are far away from the positives are labeled as “bad” keypoints. Finally, the descriptors computed at the “good” and “bad” locations form the set of features used to train the classifier that will judge each pixel of an unseen input image as a good or bad candidate for driving the extraction of a set of keypoints. This approach requires, though, the descriptors to be computed at every pixel of the image and this leads to a high computational effort. Moreover, if a certain descriptor extractor is used during the training step, it must be used also during the testing. In order to overcome these problems, the last part of this thesis has been focused on the creation and training of a convolutional neural network (CNN) that uses as positive samples the patches centered at those locations that give correct correspondences during the matching step. Eventually, the results and the performances of the developed algorithm have compared to the state-of-the-art using a public benchmark.
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Hansen, Peter Ian. "Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37667/1/Peter_Hansen_Thesis.pdf.

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This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle cameras. The ability to accurately estimate camera egomotion is a fundamental primitive of visual odometry, and this is one of the reasons for the increased popularity in the use of wide-angle cameras for this task. Their large field of view also enables them to capture images of the same regions in a scene taken at very different viewpoints, and this makes them suited for visual place recognition. However, the ability to estimate the camera egomotion and recognise the same scene in two different images is dependent on the ability to reliably detect and describe the same scene points, or ‘keypoints’, in the images. Most algorithms used for this purpose are designed almost exclusively for perspective images. Applying algorithms designed for perspective images directly to wide-angle images is problematic as no account is made for the image distortion. The primary contribution of this thesis is the development of two novel keypoint detectors, and a method of keypoint description, designed for wide-angle images. Both reformulate the Scale- Invariant Feature Transform (SIFT) as an image processing operation on the sphere. As the image captured by any central projection wide-angle camera can be mapped to the sphere, applying these variants to an image on the sphere enables keypoints to be detected in a manner that is invariant to image distortion. Each of the variants is required to find the scale-space representation of an image on the sphere, and they differ in the approaches they used to do this. Extensive experiments using real and synthetically generated wide-angle images are used to validate the two new keypoint detectors and the method of keypoint description. The best of these two new keypoint detectors is applied to vision based localisation tasks including visual odometry and visual place recognition using outdoor wide-angle image sequences. As part of this work, the effect of keypoint coordinate selection on the accuracy of egomotion estimates using the Direct Linear Transform (DLT) is investigated, and a simple weighting scheme is proposed which attempts to account for the uncertainty of keypoint positions during detection. A word reliability metric is also developed for use within a visual ‘bag of words’ approach to place recognition.
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Fefilatyev, Sergiy. "Algorithms for Visual Maritime Surveillance with Rapidly Moving Camera." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4037.

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Visual surveillance in the maritime domain has been explored for more than a decade. Although it has produced a number of working systems and resulted in a mature technology, surveillance has been restricted to the port facilities or areas close to the coastline assuming a fixed-camera scenario. This dissertation presents several contributions in the domain of maritime surveillance. First, a novel algorithm for open-sea visual maritime surveillance is introduced. We explore a challenging situation with a camera mounted on a buoy or other floating platform. The developed algorithm detects, localizes, and tracks ships in the field of view of the camera. Specifically, our method is uniquely designed to handle a rapidly moving camera. Its performance is robust in the presence of a random relatively-large camera motion. In the context of ship detection, a new horizon detection scheme for a complex maritime domain is also developed. Second, the performance of the ship detection algorithm is evaluated on a dataset of 55,000 images. Accuracy of detection of up to 88% of ships is achieved. Lastly, we consider the topic of detection of the vanishing line of the ocean surface plane as a way to estimate the horizon in difficult situations. This allows extension of the ship-detection algorithm to beyond open-sea scenarios.
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Caha, Miloš. "Určení směru pohledu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237168.

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Main object of this work is to design and implement the algorithm for look direction determination, respectively the head movement. More specifically, it is a system that searches face in the video and then detects points, suitable for view direction estimation of tracked person. Estimation is realized using searching transformation, which has been performed on key points during head movement. For accuracy enhancement the calibration frames are used. Calibration frames determines the key points transformation in defined view directions. Main result is an application able to determine deflection of head from straight position in horizontal and vertical direction for tracked person. Output doesn't contain only information about deflection direction, but it also contains the size of deflection.
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Kapoor, Prince. "Shoulder Keypoint-Detection from Object Detection." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38015.

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This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.
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Loiseau-Witon, Nicolas. "Détection et description de points clés par apprentissage." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0101.

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Les hôpitaux génèrent de plus en plus d’images médicales en 3D. Ces volumes nécessitent un recalage automatique, en vue d’être analysés de manière systématique et à grande échelle. Les points clés sont utilisés pour réduire la durée et la mémoire nécessaires à ce recalage et peuvent être détectés et décrits à l’aide de différentes méthodes classiques, mais également à l’aide de réseaux neuronaux, comme cela a été démontré de nombreuses fois en 2D. Cette thèse présente les résultats et les discussions sur les méthodes de détection et de description de points clés à l’aide de réseaux neuronaux 3D. Deux types de réseaux ont été étudiés pour détecter et/ou décrire des points caractéristiques dans des images médicales 3D. Les premiers réseaux étudiés permettent de décrire les zones entourant directement les points clés, tandis que les seconds effectuent les deux étapes de détection et de description des points clés en une seule fois
Hospitals are increasingly generating 3D medical images that require automatic registration for systematic and large-scale analysis. Key points are used to reduce the time and memory required for this registration, and can be detected and described using various classical methods, as well as neural networks, as demonstrated numerous times in 2D. This thesis presents results and discussions on methods for detecting and describing key points using 3D neural networks. Two types of networks were studied to detect and/or describe characteristic points in 3D medical images. The first networks studied describe the areas directly surrounding key points, while the second type performs both detection and description of key points in a single step
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Zhao, Mingchang. "Keypoint-Based Binocular Distance Measurement for Pedestrian Detection System on Vehicle." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31693.

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The Pedestrian Detection System (PDS) has become a significant area of research designed to protect pedestrians. Despite the huge number of research work, the most current PDSs are designed to detect pedestrians without knowing their distances from cars. In fact, a priori knowledge of the distance between a car and pedestrian allows this system to make the appropriate decision in order to avoid collisions. Typical methods of distance measurement require additional equipment (e.g., Radars) which, unfortunately, cannot identify objects. Moreover, traditional stereo-vision methods have poor precision in long-range conditions. In this thesis, we use the keypoint-based feature extraction method to generate the parallax in a binocular vision system in order to measure a detectable object; this is used instead of a disparity map. Our method enhances the tolerance to instability of a moving vehicle; and, it also enables binocular measurement systems to be equipped with a zoom lens and to have greater distance between cameras. In addition, we designed a crossover re-detection and tracking method in order to reinforce the robustness of the system (one camera helps the other reduce detection errors). Our system is able to measure the distance between cars and pedestrians; and, it can also be used efficiently to measure the distance between cars and other objects such as Traffic signs or animals. Through a real word experiment, the system shows a 7.5% margin of error in outdoor and long-range conditions.
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Eklund, Anton. "Cascade Mask R-CNN and Keypoint Detection used in Floorplan Parsing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415371.

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Parsing floorplans have been a problem in automatic document analysis for long and have up until recent years been approached with algorithmic methods. With the rise of convolutional neural networks (CNN), this problem too has seen an upswing in performance. In this thesis the task is to recover, as accurately as possible, spatial and geometric information from floorplans. This project builds around instance segmentation models like Cascade Mask R-CNN to extract the bulk of information from a floorplan image. To complement the segmentation, a new style of using keypoint-CNN is presented to find precise locations of corners. These are then combined in a post-processing step to give the resulting segmentation. The resulting segmentation scores exceed the current baseline of the CubiCasa5k floorplan dataset with a mean IoU of 72.7% compared to 57.5%. Further, the mean IoU for individual classes is also improved for almost every class. It is also shown that Cascade Mask R-CNN is better suited than Mask R-CNN for this task.
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Kemp, Neal. "Content-Based Image Retrieval for Tattoos: An Analysis and Comparison of Keypoint Detection Algorithms." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/cmc_theses/784.

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The field of biometrics has grown significantly in the past decade due to an increase in interest from law enforcement. Law enforcement officials are interested in adding tattoos alongside irises and fingerprints to their toolbox of biometrics. They often use these biometrics to aid in the identification of victims and suspects. Like facial recognition, tattoos have seen a spike in attention over the past few years. Tattoos, however, have not received as much attention by researchers. This lack of attention towards tattoos stems from the difficulty inherent in matching these tattoos. Such difficulties include image quality, affine transformation, warping of tattoos around the body, and in some cases, excessive body hair covering the tattoo. We will utilize context-based image retrieval to find a tattoo in a database which means using one image to query against a database in order to find similar tattoos. We will focus specifically on the keypoint detection process in computer vision. In addition, we are interested in finding not just exact matches but also similar tattoos. We will conclude that the ORB detector pulls the most relevant features and thus is the best chance for yielding an accurate result from content-based image retrieval for tattoos. However, we will also show that even ORB will not work on its own in a content-based image retrieval system. Other processes will have to be involved in order to return accurate matches. We will give recommendations on next-steps to create a better tattoo retrieval system.
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Book chapters on the topic "Keypoints detection"

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Cui, Yuanshun, Jie Li, Hu Han, Shiguang Shan, and Xilin Chen. "TKDN: Scene Text Detection via Keypoints Detection." In Computer Vision – ACCV 2018, 231–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20873-8_15.

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Rosenthaler, L., F. Heitger, O. Kübler, and R. von der Heydt. "Detection of general edges and keypoints." In Computer Vision — ECCV'92, 78–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55426-2_10.

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Liu, Gang, Jing Ming, Xinyun Wu, and Rifeng Jiang. "CCNet: Unpaired Keypoints for Skull Fracture Detection." In Exploration of Novel Intelligent Optimization Algorithms, 189–201. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4109-2_18.

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Rodrigues, João, and J. M. Hans du Buf. "Multi-scale Keypoints in V1 and Face Detection." In Brain, Vision, and Artificial Intelligence, 205–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11565123_21.

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Dasgupta, Madhuchhanda, and Jyotsna Kumar Mandal. "Deep Convolutional Neural Network Based Facial Keypoints Detection." In Communications in Computer and Information Science, 47–56. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8578-0_4.

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Battiato, Sebastiano, Mirko Guarnera, Tony Meccio, and Giuseppe Messina. "Red Eye Detection through Bag-of-Keypoints Classification." In Image Analysis and Processing – ICIAP 2009, 528–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04146-4_57.

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Chang, Leonardo, and José Hernández-Palancar. "A Hardware Architecture for SIFT Candidate Keypoints Detection." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 95–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_11.

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Brahmbhatt, Samarth. "Basic Machine Learning and Object Detection Based on Keypoints." In Practical OpenCV, 119–53. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-6080-6_8.

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Slot, Krzysztof, and Hyongsuk Kim. "Keypoints Derivation for Object Class Detection with SIFT Algorithm." In Artificial Intelligence and Soft Computing – ICAISC 2006, 850–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11785231_89.

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Pisov, Maxim, Vladimir Kondratenko, Alexey Zakharov, Alexey Petraikin, Victor Gombolevskiy, Sergey Morozov, and Mikhail Belyaev. "Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 723–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59725-2_70.

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Conference papers on the topic "Keypoints detection"

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Haavisto, Mikko, Arto Kaarna, and Lasse Lensu. "Deep Learning for Facial Keypoints Detection." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005272202890296.

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Jinliang, Yao, Wang Xiaohua, and Wang Rongbo. "Copy image detection based on local keypoints." In 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2011. http://dx.doi.org/10.1109/socpar.2011.6089117.

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Sarwas, Grzegorz, Slawomir Skoneczny, and Grzegorz Kurzejamski. "Fractional order method of image keypoints detection." In 2017 Signal Processing: Algorithms, Architectures, Arrangements and Applications (SPA). IEEE, 2017. http://dx.doi.org/10.23919/spa.2017.8166891.

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Li, Hao-Ting, Tz-Ting Huang, and Chen-Kuo Chiang. "Keypoints Detection for Stroke Order of Chinese Characters." In 2020 Indo-Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). IEEE, 2020. http://dx.doi.org/10.1109/indo-taiwanican48429.2020.9181315.

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Debbarma, Swapan, Angom Buboo Singh, and Kh Manglem Singh. "Keypoints based copy-move forgery detection of digital images." In 2014 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2014. http://dx.doi.org/10.1109/iciev.2014.7135994.

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Liu, Deqi, Aimin Li, Mengfan Cheng, Dexu Yao, and Xiaohan Liu. "A Multi-correspondence Object Detection Algorithm Based on Keypoints." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191943.

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"Keypoints Detection in RGB-D Space - A Hybrid Approach." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004305004960499.

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Barabanau, Ivan, Alexey Artemov, Evgeny Burnaev, and Vyacheslav Murashkin. "Monocular 3D Object Detection via Geometric Reasoning on Keypoints." In 15th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009102506520659.

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Maya, N., V. R. Bindu, and M. S. Greeshma. "An Effective Keypoints Extraction Scheme for Image Tampering Detection." In 2021 International Conference on Artificial Intelligence and Machine Vision (AIMV). IEEE, 2021. http://dx.doi.org/10.1109/aimv53313.2021.9671014.

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Lowhorn, Evan, and Rami J. Haddad. "Rules-Based Distracted Driving Detection System Using Facial Keypoints." In 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2022. http://dx.doi.org/10.1109/idsta55301.2022.9923045.

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