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Littérature scientifique sur le sujet « Keypoint-based »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Keypoint-based"

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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

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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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Thèses sur le sujet "Keypoint-based"

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Dragon, Ralf [Verfasser]. "Keypoint-Based Object Segmentation / Ralf Dragon." Aachen : Shaker, 2013. http://d-nb.info/1051573521/34.

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Zhao, Mingchang. "Keypoint-Based Binocular Distance Measurement for Pedestrian Detection System on Vehicle." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31693.

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The Pedestrian Detection System (PDS) has become a significant area of research designed to protect pedestrians. Despite the huge number of research work, the most current PDSs are designed to detect pedestrians without knowing their distances from cars. In fact, a priori knowledge of the distance between a car and pedestrian allows this system to make the appropriate decision in order to avoid collisions. Typical methods of distance measurement require additional equipment (e.g., Radars) which, unfortunately, cannot identify objects. Moreover, traditional stereo-vision methods have poor preci

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Марченко, Ігор Олександрович, Игорь Александрович Марченко, Ihor Oleksandrovych Marchenko, et al. "Usage of keypoint descriptors based algorithms for real-time objects localization." Thesis, Центральноукраїнський національний технічний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/68603.

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In order to achieve high level of security in our everyday life we produce huge amount of data. Significant part of information is presented by videos, sounds or images. A computer is used to extract useful information from raw data [1]. Pattern recognition is branch of computer vision, which allows us to get information from images [2] and videos. Information extraction is crucial problem of pattern recognition. This problem is divided into next branches: object presence; object localization; object classification.

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MAZZINI, DAVIDE. "Local Detectors and Descriptors for Object and Scene Recognition." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2018. http://hdl.handle.net/10281/199003.

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Lo scopo di questa tesi è di studiare due principali categorie di algoritmi per la detection di oggetti e il loro uso in particolari applicazioni. La prima categoria esaminata riguarda approcci basati su Keypoint. Diversi esperimenti comparativi vengono eseguiti all'interno della pipeline standard del modello di test MPEG CDVS e viene proposta una pipeline estesa che fa uso di informazione colore. La seconda categoria di object detectors oggetto di indagine si basa su Reti neurali convoluzionali. In particolare, vengono affrontate due applicazioni di reti neurali convoluzionali per il riconosc

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Bendale, Pashmina Ziparu. "Development and evaluation of a multiscale keypoint detector based on complex wavelets." Thesis, University of Cambridge, 2011. https://www.repository.cam.ac.uk/handle/1810/252226.

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Kemp, Neal. "Content-Based Image Retrieval for Tattoos: An Analysis and Comparison of Keypoint Detection Algorithms." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/cmc_theses/784.

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The field of biometrics has grown significantly in the past decade due to an increase in interest from law enforcement. Law enforcement officials are interested in adding tattoos alongside irises and fingerprints to their toolbox of biometrics. They often use these biometrics to aid in the identification of victims and suspects. Like facial recognition, tattoos have seen a spike in attention over the past few years. Tattoos, however, have not received as much attention by researchers. This lack of attention towards tattoos stems from the difficulty inherent in matching these tattoos. Such diff

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Hansen, Peter Ian. "Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37667/1/Peter_Hansen_Thesis.pdf.

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This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle

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Buck, Robert. "Cluster-Based Salient Object Detection Using K-Means Merging and Keypoint Separation with Rectangular Centers." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4631.

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The explosion of internet traffic, advent of social media sites such as Facebook and Twitter, and increased availability of digital cameras has saturated life with images and videos. Never before has it been so important to sift quickly through large amounts of digital information. Salient Object Detection (SOD) is a computer vision topic that finds methods to locate important objects in pictures. SOD has proven to be helpful in numerous applications such as image forgery detection and traffic sign recognition. In this thesis, I outline a novel SOD technique to automatically isolate important

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Liu, Wen-Pin, and 劉文彬. "A face recognition system based on keypoint exclusion and dual keypoint detection." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/02572728630414645978.

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碩士<br>銘傳大學<br>電腦與通訊工程學系碩士班<br>103<br>This thesis presents a face recognition system based on keypoint exclusion and dual keypoiont detection. There are three major problems with conventional SIFT (Scale Invariant Feature Transform). (1) It uses single type keypoint detector. For images of small size the number of detected keypoints may be too small and this causes difficulties on image matching. (2) Each keypoint of the test image is matched independently against all keypoints of the training images. This is very time consuming. (3) Only similarities between descriptors are compared and this m

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Chen, Yi-An, and 陳翊安. "CREAK : Color-based REtinA Keypoint descriptor." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/96ke4e.

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碩士<br>國立交通大學<br>多媒體工程研究所<br>104<br>Feature matching between images is key to many computer vision applications. Effective Feature matching requires effective feature description. Recently, binary descriptors which are used to describe feature points are attracting increasing attention for their low computational complexity and small memory requirement. However, most binary descriptors are based on intensity comparisons of grayscale images and did not consider color information. In this paper, a novel binary descriptor inspired by human retina is proposed, which considers not only gray values o

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Chapitres de livres sur le sujet "Keypoint-based"

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Her, Paris, Logan Manderle, Philipe A. Dias, Henry Medeiros, and Francesca Odone. "Keypoint-Based Gaze Tracking." In Pattern Recognition. ICPR International Workshops and Challenges. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68790-8_12.

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Rajput, G. G., Smruti Dilip Dabhole, and Prashantha. "Keypoint Based Tampered Image Identification." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1323-3_38.

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Zeng, Ming, Jian Liu, Youfu Li, Qinghao Meng, Ting Yang, and Zhengbiao Bai. "Keypoint-Based Enhanced Image Quality Assessment." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22456-0_60.

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Rajput, G. G., and Smruti Dilip Dabhole. "Keypoint-Based Copy-Move Area Detection." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4071-4_40.

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Wang, Shen, Xunzhi Jiang, Xiangzhan Yu, and Shuai Sun. "KCFuzz: Directed Fuzzing Based on Keypoint Coverage." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78609-0_27.

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Avola, Danilo, Marco Bernardi, Marco Cascio, Luigi Cinque, Gian Luca Foresti, and Cristiano Massaroni. "A New Descriptor for Keypoint-Based Background Modeling." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30642-7_2.

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Weinman, Jerod, Serge Belongie, and Stella Frank. "Descriptive Attributes for Language-Based Object Keypoint Detection." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44137-0_37.

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Grycuk, Rafał, Magdalena Scherer, and Sviatoslav Voloshynovskiy. "Local Keypoint-Based Image Detector with Object Detection." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59063-9_45.

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Cataño, M. A., and J. Climent. "Keypoint Detection Based on the Unimodality Test of HOGs." In Advances in Visual Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_19.

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Safdarnejad, S. Morteza, Yousef Atoum, and Xiaoming Liu. "Temporally Robust Global Motion Compensation by Keypoint-Based Congealing." In Computer Vision – ECCV 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46466-4_7.

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Actes de conférences sur le sujet "Keypoint-based"

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Hou, Xiangdan, Quansong Chen, and Hongpu Liu. "A Keypoint-Based Object Detection Algorithm." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733036.

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Venjarski, Jaroslav, L'uboš Likó, Šimon Tibenský, Marek Vančo, and Gregor Rozinaj. "Keypoint-Based Foreground-Background Image Segmentation." In 2024 International Symposium ELMAR. IEEE, 2024. http://dx.doi.org/10.1109/elmar62909.2024.10694427.

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Ye, Xingyuan, Dan Lin, and Kim-Hui Yap. "ViPose: Keypoint Visibility-Based Human Pose Estimation." In 2024 15th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2024. https://doi.org/10.1109/ictc62082.2024.10826623.

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Alkhadour, Wissam, Jamal Zraqou, and Jawad Alkhateeb. "Learning-Based Outlier Rejection for Keypoint Matching." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013218.

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Rasyid, Muhammad Ryan, Mahmud Dwi Sulistiyo, Febryanti Sthevanie, Gia Septiana Wulandari, and Deny Haryadi. "Real-time Guitar Chord Recognition Using Keypoint Detection and Neural Network-Based Inter-Keypoint Distance Classifier." In 2024 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2024. http://dx.doi.org/10.1109/icodsa62899.2024.10651791.

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Amiri, Niloufar, Guanghui Wang, and Farrokh Janabi-Sharifi. "Keypoint Detection Technique for Image-Based Visual Servoing of Manipulators." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711798.

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Pambudi, Sevito Fernanda, Eko Mulyanto Yuniamo, and Reza Fuad Rachmadi. "Lip Movement Recognition Based on Keypoint Vector using Deep Learning." In 2024 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2024. https://doi.org/10.1109/icitsi65188.2024.10929448.

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Zhou, Keyu, Wangshu Yao, Siyu Yang, and Siyuan Peng. "SalientPoint: A Self-Supervised Keypoint Detection Based on Saliency of Descriptor." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651150.

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Ju, Fujiao, Yuan Li, and Jingxin Zhao. "Unsupervised Keypoint-Based 2D/3D Registration in Minimally Invasive Pelvic Surgery." In 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). IEEE, 2025. https://doi.org/10.1109/ainit65432.2025.11035310.

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Marsicano, João Aires, Felipe Tourinho, Marco Soprani Tayar, and Marcelo Becker. "Exploring the Impact of Keypoint Precision on Vision-Based Autonomous Navigation." In 2025 Brazilian Conference on Robotics (CROS). IEEE, 2025. https://doi.org/10.1109/cros66186.2025.11066144.

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