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Journal articles on the topic 'Keypoint-based'

Author: Grafiati

Published: 9 March 2023

Last updated: 19 July 2025

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Tavakol, Ali, and Mohammad Soltanian. "Fast Feature-Based Template Matching, Based on Efficient Keypoint Extraction." Advanced Materials Research 341-342 (September 2011): 798–802. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.798.

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In order to improve the performance of feature-based template matching techniques, several research papers have been published. Real-time applications require the computational complexity of keypoint matching algorithms to be as low as possible. In this paper, we propose a method to improve the keypoint detection stage of feature-based template matching algorithms. Our experiment results show that the proposed method outperforms keypoint matching techniques in terms of speed, keypoint stability and repeatability.

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Guan, Genliang, Zhiyong Wang, Shiyang Lu, Jeremiah Da Deng, and David Dagan Feng. "Keypoint-Based Keyframe Selection." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 4 (2013): 729–34. http://dx.doi.org/10.1109/tcsvt.2012.2214871.

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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 (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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Isfort, Steffen, Melanie Elias, and Hans-Gerd Maas. "Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-2-2024 (June 10, 2024): 113–20. http://dx.doi.org/10.5194/isprs-annals-x-2-2024-113-2024.

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Abstract. Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector.

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Weinreb, Caleb, Jonah E. Pearl, Sherry Lin, et al. "Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics." Nature Methods 21, no. 7 (2024): 1329–39. http://dx.doi.org/10.1038/s41592-024-02318-2.

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AbstractKeypoint tracking algorithms can flexibly quantify animal movement from videos obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into discrete actions. This challenge is particularly acute because keypoint data are susceptible to high-frequency jitter that clustering algorithms can mistake for transitions between actions. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules (‘syllables’) from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to identify syllables whose boundaries correspond to natural sub-second discontinuities in pose dynamics. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq also works in multiple species and generalizes beyond the syllable timescale, identifying fast sniff-aligned movements in mice and a spectrum of oscillatory behaviors in fruit flies. Keypoint-MoSeq, therefore, renders accessible the modular structure of behavior through standard video recordings.

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Yu, Ning, Yongping Tian, Xiaochuan Zhang, and Xiaofeng Yin. "Face Keypoint Detection Method Based on Blaze_ghost Network." Applied Sciences 13, no. 18 (2023): 10385. http://dx.doi.org/10.3390/app131810385.

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The accuracy and speed of facial keypoint detection are crucial factors for effectively extracting fatigue features, such as eye blinking and yawning. This paper focuses on the improvement and optimization of facial keypoint detection algorithms, presenting a facial keypoint detection method based on the Blaze_ghost network and providing more reliable support for facial fatigue analysis. Firstly, the Blaze_ghost network is designed as the backbone network with a deeper structure and more parameters to better capture facial detail features, improving the accuracy of keypoint localization. Secondly, HuberWingloss is designed as the loss function to further reduce the training difficulty of the model and enhance its generalization ability. Compared to traditional loss functions, HuberWingloss can reduce the interference of outliers (such as noise and occlusion) in model training, improve the model’s robustness to complex situations, and further enhance the accuracy of keypoint detection. Experimental results show that the proposed method achieves significant improvements in both the NME (Normal Mean Error) and FR (Failure Rate) evaluation metrics. Compared to traditional methods, the proposed model demonstrates a considerable improvement in keypoint localization accuracy while still maintaining high detection efficiency.

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Boonsivanon, Krittachai, and Worawat Sa-Ngiamvibool. "A SIFT Description Approach for Non-Uniform Illumination and Other Invariants." Ingénierie des systèmes d information 26, no. 6 (2021): 533–39. http://dx.doi.org/10.18280/isi.260603.

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The new improvement keypoint description technique of image-based recognition for rotation, viewpoint and non-uniform illumination situations is presented. The technique is relatively simple based on two procedures, i.e., the keypoint detection and the keypoint description procedure. The keypoint detection procedure is based on the SIFT approach, Top-Hat filtering, morphological operations and average filtering approach. Where this keypoint detection procedure can segment the targets from uneven illumination particle images. While the keypoint description procedures are described and implemented using the Hu moment invariants. Where the central moments are being unchanged under image translations. The sensitivity, accuracy and precision rate of data sets were evaluated and compared. The data set are provided by color image database with variants uniform and non-uniform illumination, viewpoint and rotation changes. The evaluative results show that the approach is superior to the other SIFTs in terms of uniform illumination, non-uniform illumination and other situations. Additionally, the paper demonstrates the high sensitivity of 100%, high accuracy of 83.33% and high precision rate of 80.00%. Comparisons to other SIFT approaches are also included.

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Cevahir, Ali, and Junji Torii. "High Performance Online Image Search with GPUs on Large Image Databases." International Journal of Multimedia Data Engineering and Management 4, no. 3 (2013): 24–41. http://dx.doi.org/10.4018/jmdem.2013070102.

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The authors propose an online image search engine based on local image keypoint matching with GPU support. State-of-the-art models are based on bag-of-visual-words, which is an analogy of textual search for visual search. In this work, thanks to the vector computation power of the GPU, the authors utilize real values of keypoint descriptors and realize real-time search at keypoint level. By keeping the identities of each keypoint, closest keypoints are accurately retrieved. Image search has different characteristics than textual search. The authors implement one-to-one keypoint matching, which is more natural for images. The authors utilize GPUs for every basic step. To demonstrate practicality of GPU-extended image search, the authors also present a simple bag-of-visual-words search technique with full-text search engines. The authors explain how to implement one-to-one keypoint matching with text search engine. Proposed methods lead to drastic performance and precision improvement, which is demonstrated on datasets of different sizes.

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Feng, Lu, Quan Fu, Xiang Long, and Zhuang Zhi Wu. "Keypoint Recognition for 3D Head Model Using Geometry Image." Applied Mechanics and Materials 654 (October 2014): 287–90. http://dx.doi.org/10.4028/www.scientific.net/amm.654.287.

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This paper presents a novel and efficient 3D head model keypoint recognition framework based on the geometry image. Based on conformal mapping and diffusion scale space, our method can utilize the SIFT method to extract and describe the keypoint of 3D head model. We use this framework to identify the keypoint of the human head. The experiments shows the robust and efficiency of our method.

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Liu, Weiyu, and Nan Di. "RSCS6D: Keypoint Extraction-Based 6D Pose Estimation." Applied Sciences 15, no. 12 (2025): 6729. https://doi.org/10.3390/app15126729.

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In this work, we propose an improved network, RSCS6D, for 6D pose estimation from RGB-D images by extracting keypoint-based point clouds. Our key insight is that keypoint cloud can reduce data redundancy in 3D point clouds and accelerate the convergence of convolutional neural networks. First, we employ a semantic segmentation network on the RGB image to obtain mask images containing positional information and per-pixel labels. Next, we introduce a novel keypoint cloud extraction algorithm that combines RGB and depth images to detect 2D keypoints and convert them into 3D keypoints. Specifically, we convert the RGB image to grayscale and use the Sobel edge detection operator to identify 2D edge keypoints. Additionally, we compute the Curvature matrix from the depth image and apply the Sobel operator to extract keypoints critical for 6D pose estimation. Finally, the extracted 3D keypoint cloud is fed into the 6D pose estimation network to predict both translation and rotation.

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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 (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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Wu, Zhonghua, Guosheng Lin, and Jianfei Cai. "Keypoint based weakly supervised human parsing." Image and Vision Computing 91 (November 2019): 103801. http://dx.doi.org/10.1016/j.imavis.2019.08.005.

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Ding, Xintao, Qingde Li, Yongqiang Cheng, Jinbao Wang, Weixin Bian, and Biao Jie. "Local keypoint-based Faster R-CNN." Applied Intelligence 50, no. 10 (2020): 3007–22. http://dx.doi.org/10.1007/s10489-020-01665-9.

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Huang, Canyu, Zeyong Lei, Linhui Li, Lin Zhong, Jieheng Lei, and Shuiming Wang. "A Method for Detecting Key Points of Transferring Barrel Valve by Integrating Keypoint R-CNN and MobileNetV3." Electronics 12, no. 20 (2023): 4306. http://dx.doi.org/10.3390/electronics12204306.

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Industrial robots need to accurately identify the position and rotation angle of the handwheel of chemical raw material barrel valves during the process of opening and closing, in order to avoid interference between the robot gripper and the handwheel. This paper proposes a handwheel keypoint detection algorithm for fast and accurate acquisition of handwheel position and rotation pose. The algorithm is based on the Keypoint R-CNN (Region-based Convolutional Neural Network) keypoint detection model, which integrates the lightweight mobile network MobileNetV3, the Coordinate Attention module, and improved BiFPN (Bi-directional Feature Pyramid Network) structure to improve the detection speed of the model, enhance the feature extraction performance of the handwheel, and improve the expression capability of small targets at keypoint locations. Experimental results on a self-built handwheel dataset demonstrate that the proposed algorithm outperforms the Keypoint R-CNN model in terms of detection speed and accuracy, with a speed improvement of 54.6%. The detection accuracy and keypoint detection accuracy reach 93.3% and 98.7%, respectively, meeting the requirements of the application scenario and enabling accurate control of the robot’s rotation of the valve handwheel.

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Wang, Yu, Xiaoye Wang, Zaiwang Gu, et al. "SuperJunction: Learning-Based Junction Detection for Retinal Image Registration." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (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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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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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 (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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B.Daneshvar, M. "SCALE INVARIANT FEATURE TRANSFORM PLUS HUE FEATURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W6 (August 23, 2017): 27–32. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w6-27-2017.

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This paper presents an enhanced method for extracting invariant features from images based on Scale Invariant Feature Transform (SIFT). Although SIFT features are invariant to image scale and rotation, additive noise, and changes in illumination but we think this algorithm suffers from excess keypoints. Besides, by adding the hue feature, which is extracted from combination of hue and illumination values in HSI colour space version of the target image, the proposed algorithm can speed up the matching phase. Therefore, we proposed the Scale Invariant Feature Transform plus Hue (SIFTH) that can remove the excess keypoints based on their Euclidean distances and adding hue to feature vector to speed up the matching process which is the aim of feature extraction. In this paper we use the difference of hue features and the Mean Square Error (MSE) of orientation histograms to find the most similar keypoint to the under processing keypoint. The keypoint matching method can identify correct keypoint among clutter and occlusion robustly while achieving real-time performance and it will result a similarity factor of two keypoints. Moreover removing excess keypoint by SIFTH algorithm helps the matching algorithm to achieve this goal.

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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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Xu, Shaoyan, Tao Wang, Congyan Lang, Songhe Feng, and Yi Jin. "Graph-based visual odometry for VSLAM." Industrial Robot: An International Journal 45, no. 5 (2018): 679–87. http://dx.doi.org/10.1108/ir-04-2018-0061.

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Purpose Typical feature-matching algorithms use only unary constraints on appearances to build correspondences where little structure information is used. Ignoring structure information makes them sensitive to various environmental perturbations. The purpose of this paper is to propose a novel graph-based method that aims to improve matching accuracy by fully exploiting the structure information. Design/methodology/approach Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner. Findings The authors compare it with several state-of-the-art visual simultaneous localization and mapping algorithms on three datasets. Experimental results reveal that the ORB-G algorithm provides more accurate and robust trajectories in general. Originality/value Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.

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Huang, Chen-Wei, and Jian-Jiun Ding. "Adaptive Superpixel-Based Disparity Estimation Algorithm Using Plane Information and Disparity Refining Mechanism in Stereo Matching." Symmetry 14, no. 5 (2022): 1005. http://dx.doi.org/10.3390/sym14051005.

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The motivation of this paper is to address the limitations of the conventional keypoint-based disparity estimation methods. Conventionally, disparity estimation is usually based on the local information of keypoints. However, keypoints may distribute sparsely in the smooth region, and keypoints with the same descriptors may appear in a symmetric pattern. Therefore, conventional keypoint-based disparity estimation methods may have limited performance in smooth and symmetric regions. The proposed algorithm is superpixel-based. Instead of performing keypoint matching, both keypoint and semiglobal information are applied to determine the disparity in the proposed algorithm. Since the local information of keypoints and the semi-global information of the superpixel are both applied, the accuracy of disparity estimation can be improved, especially for smooth and symmetric regions. Moreover, to address the non-uniform distribution problem of keypoints, a disparity refining mechanism based on the similarity and the distance of neighboring superpixels is applied to correct the disparity of the superpixel with no or few keypoints. The experiments show that the disparity map generated by the proposed algorithm has a lower matching error rate than that generated by other methods.

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Morgacheva, A. I., V. A. Kulikov, and V. P. Kosykh. "DYNAMIC KEYPOINT-BASED ALGORITHM OF OBJECT TRACKING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 79–82. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-79-2017.

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The model of the observed object plays the key role in the task of object tracking. Models as a set of image parts, in particular, keypoints, is more resistant to the changes in shape, texture, angle of view, because local changes apply only to specific parts of the object. On the other hand, any model requires updating as the appearance of the object changes with respect to the camera. In this paper, we propose a dynamic (time-varying) model, based on a set of keypoints. To update the data this model uses the algorithm of rating keypoints and the decision rule, based on a Function of Rival Similarity (FRiS). As a result, at the test set of image sequences the improvement was achieved on average by 9.3% compared to the original algorithm. On some sequences, the improvement was 16% compared to the original algorithm.

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ATİK, Muhammed Enes, Abdullah Harun İNCEKARA, Batuhan SARITÜRK, Ozan ÖZTÜRK, Zaide DURAN, and Dursun Zafer ŞEKER. "3D Object Recognition with Keypoint Based Algorithms." International Journal of Environment and Geoinformatics 6, no. 1 (2019): 139–42. http://dx.doi.org/10.30897/ijegeo.551747.

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Gong, Caili, Yong Zhang, Yongfeng Wei, Xinyu Du, Lide Su, and Zhi Weng. "Multicow pose estimation based on keypoint extraction." PLOS ONE 17, no. 6 (2022): e0269259. http://dx.doi.org/10.1371/journal.pone.0269259.

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Automatic estimation of the poses of dairy cows over a long period can provide relevant information regarding their status and well-being in precision farming. Due to appearance similarity, cow pose estimation is challenging. To monitor the health of dairy cows in actual farm environments, a multicow pose estimation algorithm was proposed in this study. First, a monitoring system was established at a dairy cow breeding site, and 175 surveillance videos of 10 different cows were used as raw data to construct object detection and pose estimation data sets. To achieve the detection of multiple cows, the You Only Look Once (YOLO)v4 model based on CSPDarkNet53 was built and fine-tuned to output the bounding box for further pose estimation. On the test set of 400 images including single and multiple cows throughout the whole day, the average precision (AP) reached 94.58%. Second, the keypoint heatmaps and part affinity field (PAF) were extracted to match the keypoints of the same cow based on the real-time multiperson 2D pose detection model. To verify the performance of the algorithm, 200 single-object images and 200 dual-object images with occlusions were tested under different light conditions. The test results showed that the AP of leg keypoints was the highest, reaching 91.6%, regardless of day or night and single cows or double cows. This was followed by the AP values of the back, neck and head, sequentially. The AP of single cow pose estimation was 85% during the day and 78.1% at night, compared to double cows with occlusion, for which the values were 74.3% and 71.6%, respectively. The keypoint detection rate decreased when the occlusion was severe. However, in actual cow breeding sites, cows are seldom strongly occluded. Finally, a pose classification network was built to estimate the three typical poses (standing, walking and lying) of cows based on the extracted cow skeleton in the bounding box, achieving precision of 91.67%, 92.97% and 99.23%, respectively. The results showed that the algorithm proposed in this study exhibited a relatively high detection rate. Therefore, the proposed method can provide a theoretical reference for animal pose estimation in large-scale precision livestock farming.

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Pieropan, Alessandro, Niklas Bergström, Masatoshi Ishikawa, and Hedvig Kjellström. "Robust and adaptive keypoint-based object tracking." Advanced Robotics 30, no. 4 (2016): 258–69. http://dx.doi.org/10.1080/01691864.2015.1129360.

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Jia Yongjie, 贾勇杰, 熊风光 Xiong Fengguang, 韩燮 Han Xie, and 况立群 Kuang Liqun. "Multi-Scale Keypoint Detection Based on SHOT." Laser & Optoelectronics Progress 55, no. 7 (2018): 071013. http://dx.doi.org/10.3788/lop55.071013.

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Agier, R., S. Valette, R. Kéchichian, L. Fanton, and R. Prost. "Hubless keypoint-based 3D deformable groupwise registration." Medical Image Analysis 59 (January 2020): 101564. http://dx.doi.org/10.1016/j.media.2019.101564.

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Rajput, G. G., Smruti Dilip Dabhole, and Prashantha. "Modified Keypoint-Based Copy Move Area Detection." Procedia Computer Science 235 (2024): 3389–96. http://dx.doi.org/10.1016/j.procs.2024.04.319.

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Su, Lide, Minghuang Li, Yong Zhang, Zheying Zong, and Caili Gong. "Fusion of Target and Keypoint Detection for Automated Measurement of Mongolian Horse Body Measurements." Agriculture 14, no. 7 (2024): 1069. http://dx.doi.org/10.3390/agriculture14071069.

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Accurate and efficient access to Mongolian horse body size information is an important component in the modernization of the equine industry. Aiming at the shortcomings of manual measurement methods, such as low efficiency and high risk, this study converts the traditional horse body measure measurement problem into a measurement keypoint localization problem and proposes a top-down automatic Mongolian horse body measure measurement method by integrating the target detection algorithm and keypoint detection algorithm. Firstly, the SimAM parameter-free attention mechanism is added to the YOLOv8n backbone network to constitute the SimAM–YOLOv8n algorithm, which provides the base image for the subsequent accurate keypoint detection; secondly, the coordinate regression-based RTMPose keypoint detection algorithm is used for model training to realize the keypoint localization of the Mongolian horse. Lastly, the cosine annealing method was employed to dynamically adjust the learning rate throughout the entire training process, and subsequently conduct body measurements based on the information of each keypoint. The experimental results show that the average accuracy of the SimAM–YOLOv8n algorithm proposed in this study was 90.1%, and the average accuracy of the RTMPose algorithm was 91.4%. Compared with the manual measurements, the shoulder height, chest depth, body height, body length, croup height, angle of shoulder and angle of croup had mean relative errors (MRE) of 3.86%, 4.72%, 3.98%, 2.74%, 2.89%, 4.59% and 5.28%, respectively. The method proposed in this study can provide technical support to realize accurate and efficient Mongolian horse measurements.

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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 (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, et al. "KGDet: Keypoint-Guided Fashion Detection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (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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Yu, Sheng, Di-Hua Zhai, and Yuanqing Xia. "KeyPose: Category-Level 6D Object Pose Estimation with Self-Adaptive Keypoints." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9653–61. https://doi.org/10.1609/aaai.v39i9.33046.

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Category-level object pose estimation is an important task in computer vision. Some prior methods based on assumptions often struggle with drastic changes in object appearance. To address this challenge, we propose a new method for object pose estimation based on object-adaptive keypoints. In this paper, we first introduce a transformer-based keypoint prediction method for adaptive forecasting of point cloud keypoints. This method calculates the similarity between keypoint features and point cloud features, allowing keypoints to represent object geometry more effectively. Furthermore, to enhance the geometric feature construction of keypoints, we propose a graph-based keypoint feature aggregation method, which considers both the structural relationships between keypoints and the point cloud, strengthening the network's understanding of geometric structures. At this stage, keypoints remain at the geometric spatial level of the object and have not been predicted in NOCS. To improve the accuracy of keypoint prediction in NOCS, we design a NOCS voxelization method that divides NOCS into multiple voxels and accurately predicts NOCS keypoints within these voxels. Experimental results on multiple benchmark datasets demonstrate that our proposed KeyPose method outperforms all existing methods, achieving over 20% improvement in pose accuracy on some critical datasets.

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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 (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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Ramesh, Manu, and Amy R. Reibman. "SURABHI: Self-Training Using Rectified Annotations-Based Hard Instances for Eidetic Cattle Recognition." Sensors 24, no. 23 (2024): 7680. https://doi.org/10.3390/s24237680.

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We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances—instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model. We engineer this scheme for the task of predicting keypoints of cattle from the top, in conjunction with our Eidetic Cattle Recognition System, which is dependent on accurate prediction of keypoints for predicting the correct cow ID. We show that the final cow ID prediction accuracy on previously unseen cows also improves significantly after applying SURABHI to a deep-learning detection model with high capacity, especially when available training data are minimal. SURABHI helps us achieve a top-6 cow recognition accuracy of 91.89% on a dataset of cow videos. Using SURABHI on this dataset also improves the number of cow instances with correct identification by 22% over the baseline result from fully supervised training.

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Han, Shaolong, Shangrong Wang, Wenqi Liu, YongQiang Gu, and Yujie Zhang. "Swarm Intelligence-Enhanced Detection of Small Objects Using Key Point-Driven YOLO." International Journal of Swarm Intelligence Research 16, no. 1 (2025): 1–20. https://doi.org/10.4018/ijsir.368649.

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Traditional object detection methods, such as anchor-based YOLO variants, face challenges due to the irregular shapes and small sizes of these contaminants. This paper introduces a novel approach that leverages swarm Intelligence to enhance the performance of a keypoint-driven YOLO framework. By integrating keypoint detection with Boundary-Aware Vectors (BBAVectors) and utilizing swarm intelligence algorithms for model optimization, our approach improves the localization and identification of small, irregularly shaped non-metallic objects. By optimizing the feature extraction process through swarm-based techniques and incorporating keypoint-driven object detection, our model significantly boosts precision and recall compared to traditional methods. Evaluated on a custom dataset of fiber like materials, our approach achieves a mean Average Precision (mAP) of 92.9% at IoU 0.5, demonstrating strong performance in real-world applications.

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Yang, Erbing, Fei Chen, Meiqing Wang, Hang Cheng, and Rong Liu. "Local Property of Depth Information in 3D Images and Its Application in Feature Matching." Mathematics 11, no. 5 (2023): 1154. http://dx.doi.org/10.3390/math11051154.

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In image registration or image matching, the feature extracted by using the traditional methods does not include the depth information which may lead to a mismatch of keypoints. In this paper, we prove that when the camera moves, the ratio of the depth difference of a keypoint and its neighbor pixel before and after the camera movement approximates a constant. That means the depth difference of a keypoint and its neighbor pixel after normalization is invariant to the camera movement. Based on this property, all the depth differences of a keypoint and its neighbor pixels constitute a local depth-based feature, which can be used as a supplement of the traditional feature. We combine the local depth-based feature with the SIFT feature descriptor to form a new feature descriptor, and the experimental results show the feasibility and effectiveness of the new feature descriptor.

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Li, Jinxing, Yanhong Liu, Wenxin Zheng, Xinwen Chen, Yabin Ma, and Leifeng Guo. "Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model." Animals 14, no. 12 (2024): 1791. http://dx.doi.org/10.3390/ani14121791.

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Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle’s rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming.

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Liu, Xiaomin, Runqi Zhao, Jun-Bao Li, Jeng-Shyang Pan, and Huaqi Zhao. "A Point–Set–Domain Image Object Matching Method for Airborne Object Localization." Journal of Internet Technology 26, no. 3 (2025): 303–14. https://doi.org/10.70003/160792642025052603003.

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Image object localization is an important research direction in the development of intelligent autonomous control systems for unmanned aerial vehicles (UAVs). Major challenges remain, such as cross-view images, large-scale deformation, and multitemporal variation. We propose a point–set–domain matching method to locate objects. First, the property constraints of a point, including sparsity, repeatability, and distinguishability,are combined into a keypoint response used to optimize convolutional neural networks, creating keypoint detector and feature descriptor models. With these models, we can improve the performance of point matching and obtain the corresponding keypoint set accurately. This approach solves the cross-view problem. Second, a spatial transformation model of the corresponding keypoint set is obtained using keypoint-constrained diffeomorphism matching, which can align the spatial location of two images and solve the large-scale deformation problem. Third, an approach combining probability statistics with watershed maximally stable extremal regions is proposed to divide the object image and reference image into several subregions, and then the similarity based on diffeomorphism is employed to localize the object in the UAV image, which solves the multitemporal variation problem. The experimental results show that the proposed method can successfully determine the location of the object in the UAV image.

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Fincato, Matteo, and Roberto Vezzani. "DualPose: Dual-Block Transformer Decoder with Contrastive Denoising for Multi-Person Pose Estimation." Sensors 25, no. 10 (2025): 2997. https://doi.org/10.3390/s25102997.

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Multi-person pose estimation is the task of detecting and regressing the keypoint coordinates of multiple people in a single image. Significant progress has been achieved in recent years, especially with the introduction of transformer-based end-to-end methods. In this paper, we present DualPose, a novel framework that enhances multi-person pose estimation by leveraging a dual-block transformer decoding architecture. Class prediction and keypoint estimation are split into parallel blocks so each sub-task can be separately improved and the risk of interference is reduced. This architecture improves the precision of keypoint localization and the model’s capacity to accurately classify individuals. To improve model performance, the Keypoint-Block uses parallel processing of self-attentions, providing a novel strategy that improves keypoint localization accuracy and precision. Additionally, DualPose incorporates a contrastive denoising (CDN) mechanism, leveraging positive and negative samples to stabilize training and improve robustness. Thanks to CDN, a variety of training samples are created by introducing controlled noise into the ground truth, improving the model’s ability to discern between valid and incorrect keypoints. DualPose achieves state-of-the-art results outperforming recent end-to-end methods, as shown by extensive experiments on the MS COCO and CrowdPose datasets. The code and pretrained models are publicly available.

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Zhao, Yidan, Ming Chen, Guofu Feng, Wanying Zhai, Peng Xiao, and Yongxiang Huang. "Fine-Grained Fish Individual Recognition in Underwater Environments Using Global Detail Enhancement and Keypoint Region Fusion." Fishes 10, no. 3 (2025): 102. https://doi.org/10.3390/fishes10030102.

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With the rapid advancement of intelligent aquaculture, precise individual identification of underwater fish has become a crucial method for achieving smart farming. By accurately recognizing and tracking individuals within the same species, researchers can enable individual-level identification and tracking, significantly enhancing the efficiency of research and management. To address the challenges of complex underwater environments and subtle differences among similar individuals that affect recognition accuracy, this paper proposes a fish individual identification method based on global detail enhancement and keypoint region fusion. This method simultaneously learns global refined features and keypoint region features, dynamically capturing effective keypoint features while mitigating errors caused by noise through weighted fusion. The network first employs a global detail enhancement module to extract global features, such as overall morphology and texture information, followed by the extraction of fine-grained features from keypoint regions. Through the weighted fusion, the network further emphasizes critical areas, thereby enhancing robustness and adaptability in complex underwater scenarios. This design effectively integrates global refined features and local keypoint features, providing comprehensive support for accurate fish individual identification. Experimental results show that the proposed method achieves mAP and Rank-1 scores of 89.7% and 95.3%, respectively, and demonstrates strong generalization capabilities in other fish identification tasks.

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Kim, Sejun, Sungjae Kang, Hyomin Choi, Seong-Soo Kim, and Kisung Seo. "Valid Keypoint Augmentation based Occluded Person Re-Identification." Transactions of The Korean Institute of Electrical Engineers 71, no. 7 (2022): 1002–7. http://dx.doi.org/10.5370/kiee.2022.71.7.1002.

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Đăng Khuyên, Phan, Nguyễn Phi Bằng, and Đặng Thành Trung. "Enhance robustness for watermarking based on keypoint features." Journal of Science, Educational Science 60, no. 7A (2015): 169–79. http://dx.doi.org/10.18173/2354-1075.2015-0064.

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Gao, Hong-bo, Hong-yu Wang, and Xiao-kai Liu. "A Keypoint Matching Method Based on Hierarchical Learning." Journal of Electronics & Information Technology 35, no. 11 (2014): 2751–57. http://dx.doi.org/10.3724/sp.j.1146.2013.00347.

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EMAM, Mahmoud, Qi HAN, Liyang YU, and Hongli ZHANG. "A Keypoint-Based Region Duplication Forgery Detection Algorithm." IEICE Transactions on Information and Systems E99.D, no. 9 (2016): 2413–16. http://dx.doi.org/10.1587/transinf.2016edl8024.

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Yang, Lian. "New Keypoint Detector and Descriptor Based on SIFT." Journal of Information and Computational Science 12, no. 14 (2015): 5279–90. http://dx.doi.org/10.12733/jics20106500.

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Lan Jianxia, 兰渐霞, 王泽勇 Wang Zeyong, 李金龙 Li Jinlong, 袁萌 Yuan Meng, and 高晓蓉 Gao Xiaorong. "Keypoint Extraction Algorithm Based on Normal Shape Index." Laser & Optoelectronics Progress 57, no. 16 (2020): 161016. http://dx.doi.org/10.3788/lop57.161016.

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Bellavia, F., D. Tegolo, and C. Valenti. "Keypoint descriptor matching with context-based orientation estimation." Image and Vision Computing 32, no. 9 (2014): 559–67. http://dx.doi.org/10.1016/j.imavis.2014.05.002.

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Prakash, Choudhary Shyam, Hari Om, Sushila Maheshkar, Vikas Maheshkar, and Tao Song. "Keypoint-based passive method for image manipulation detection." Cogent Engineering 5, no. 1 (2018): 1523346. http://dx.doi.org/10.1080/23311916.2018.1523346.

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Li, Xuyang, Xuemei Xie, Mingxuan Yu, Jiakai Luo, Chengwei Rao, and Guangming Shi. "Gradient Corner Pooling for Keypoint-Based Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1460–67. http://dx.doi.org/10.1609/aaai.v37i2.25231.

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Detecting objects as multiple keypoints is an important approach in the anchor-free object detection methods while corner pooling is an effective feature encoding method for corner positioning. The corners of the bounding box are located by summing the feature maps which are max-pooled in the x and y directions respectively by corner pooling. In the unidirectional max pooling operation, the features of the densely arranged objects of the same class are prone to occlusion. To this end, we propose a method named Gradient Corner Pooling. The spatial distance information of objects on the feature map is encoded during the unidirectional pooling process, which effectively alleviates the occlusion of the homogeneous object features. Further, the computational complexity of gradient corner pooling is the same as traditional corner pooling and hence it can be implemented efficiently. Gradient corner pooling obtains consistent improvements for various keypoint-based methods by directly replacing corner pooling. We verify the gradient corner pooling algorithm on the dataset and in real scenarios, respectively. The networks with gradient corner pooling located the corner points earlier in the training process and achieve an average accuracy improvement of 0.2%-1.6% on the MS-COCO dataset. The detectors with gradient corner pooling show better angle adaptability for arrayed objects in the actual scene test.

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Jeong, Yongho, Taeuk Noh, Yonghak Lee, et al. "A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement." Applied Sciences 15, no. 4 (2025): 2001. https://doi.org/10.3390/app15042001.

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In this study, we propose a solution for automatically measuring body circumferences by utilizing the built-in LiDAR sensor in mobile devices. Traditional body measurement methods mainly rely on 2D images or manual measurements. This research, however, utilizes 3D depth information to enhance both accuracy and efficiency. By employing HRNet-based keypoint detection and transfer learning through deep learning, the precise locations of body parts are identified and combined with depth maps to automatically calculate body circumferences. Experimental results demonstrate that the proposed method exhibits a relative error of up to 8% for major body parts such as waist, chest, hip, and buttock circumferences, with waist and buttock measurements recording low error rates below 4%. Although some models showed error rates of 7.8% and 7.4% in hip circumference measurements, this was attributed to the complexity of 3D structures and the challenges in selecting keypoint locations. Additionally, the use of depth map-based keypoint correction and regression analysis significantly improved accuracy compared to conventional 2D-based measurement methods. The real-time processing speed was also excellent, ensuring stable performance across various body types.

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