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Journal articles on the topic 'Keypoint-based'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

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

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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

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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 distingui

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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. Secon

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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 implement

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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

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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. Specificall

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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 t

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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, an

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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 m

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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 fo

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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 descript

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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

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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 fas

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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 in

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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

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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 Si

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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 co

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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 YOLOv8

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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 separatio

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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 bene

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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 enhan

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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 C

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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 ke

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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 s

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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 dept

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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

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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 ca

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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 impr

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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 det

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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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Abstract:

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

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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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Abstract:

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 e

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