Relevant bibliographies by topics / Keypoint-based

Contents

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

Academic literature on the topic 'Keypoint-based'

Author: Grafiati
Published: 9 March 2023
Last updated: 19 July 2025

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Journal articles on the topic "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 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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Dissertations / Theses on the topic "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 precision in long-range conditions. In this thesis, we use the keypoint-based feature extraction method to generate the parallax in a binocular vision system in order to measure a detectable object; this is used instead of a disparity map. Our method enhances the tolerance to instability of a moving vehicle; and, it also enables binocular measurement systems to be equipped with a zoom lens and to have greater distance between cameras. In addition, we designed a crossover re-detection and tracking method in order to reinforce the robustness of the system (one camera helps the other reduce detection errors). Our system is able to measure the distance between cars and pedestrians; and, it can also be used efficiently to measure the distance between cars and other objects such as Traffic signs or animals. Through a real word experiment, the system shows a 7.5% margin of error in outdoor and long-range conditions.
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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 riconoscimento di oggetti. Il primo riguarda il riconoscimento di loghi commerciali. Due pipeline di classificazione sono progettate e testate su un set di immagini raccolte da Flickr. La prima architettura utilizza una rete pre-addestrata come feature extractor e raggiunge risultati comparabili a quelli di algoritmi basati Keypoint. La seconda architettura si avvale di una rete neurale che supera le performances di metodi stato dell'arte basati su Keypoint. L'altra applicazione esaminata è la categorizzazione di dipinti che consiste nell'associare l'autore, nell'assegnare un dipinto alla scuola o al movimento artistico a cui appartiene, e classificare il genere del dipinto, ad es. paesaggio, ritratto, illustrazione ecc. Per affrontare questo problema, viene proposta una struttura di rete neurale multibranch e multitask che beneficia dell'uso congiunto di approcci basati su keypoint e di features neurali. In entrambe le applicazioni viene anche esaminato l'uso di tecniche di data augmentation per ampliare il training set. In particolare per i dipinti, un algoritmo di trasferimento di stile pittorico basato su reti neurali viene sfruttato per generare quadri sintetici da utilizzare in fase di training.<br>The aim of this thesis is to study two main categories of algorithms for object detection and their use in particular applications. The first category that is investigated concerns Keypoint-based approaches. Several comparative experiments are performed within the standard testing pipeline of the MPEG CDVS Test Model and an extended pipeline which make use of color information is proposed. The second category of object detectors that is investigated is based on Convolutional Neural Networks. Two applications of Convolutional Neural Networks for object recognition are in particular addressed. The first concerns logo recognition. Two classification pipelines are designed and tested on a real-world dataset of images collected from Flickr. The first architecture makes use of a pre-trained network as feature extractor and it achieves comparable results keypoint based approaches. The second architecture makes use of a tiny end-to-end trained Neural Network that outperformed state-of-the-art keypoint based methods. The other application addressed is Painting Categorization. It consists in associating the author, assigning a painting to the school or art movement it belongs to, and categorizing the genre of the painting, e.g. landscape, portrait, illustration etc. To tackle this problem, a novel multibranch and multitask Neural Network structure is proposed which benefit from joint use of keypoint-based approaches and neural features. In both applications the use of data augmentation techniques to enlarge the training set is also investigated. In particular for paintings, a neural style transfer algorithm is exploited for generating synthetic paintings to be used in training.
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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 difficulties include image quality, affine transformation, warping of tattoos around the body, and in some cases, excessive body hair covering the tattoo. We will utilize context-based image retrieval to find a tattoo in a database which means using one image to query against a database in order to find similar tattoos. We will focus specifically on the keypoint detection process in computer vision. In addition, we are interested in finding not just exact matches but also similar tattoos. We will conclude that the ORB detector pulls the most relevant features and thus is the best chance for yielding an accurate result from content-based image retrieval for tattoos. However, we will also show that even ORB will not work on its own in a content-based image retrieval system. Other processes will have to be involved in order to return accurate matches. We will give recommendations on next-steps to create a better tattoo retrieval system.
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Hansen, Peter Ian. "Wide-baseline keypoint detection and matching with wide-angle images for vision based localisation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37667/1/Peter_Hansen_Thesis.pdf.

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This thesis addresses the problem of detecting and describing the same scene points in different wide-angle images taken by the same camera at different viewpoints. This is a core competency of many vision-based localisation tasks including visual odometry and visual place recognition. Wide-angle cameras have a large field of view that can exceed a full hemisphere, and the images they produce contain severe radial distortion. When compared to traditional narrow field of view perspective cameras, more accurate estimates of camera egomotion can be found using the images obtained with wide-angle cameras. The ability to accurately estimate camera egomotion is a fundamental primitive of visual odometry, and this is one of the reasons for the increased popularity in the use of wide-angle cameras for this task. Their large field of view also enables them to capture images of the same regions in a scene taken at very different viewpoints, and this makes them suited for visual place recognition. However, the ability to estimate the camera egomotion and recognise the same scene in two different images is dependent on the ability to reliably detect and describe the same scene points, or ‘keypoints’, in the images. Most algorithms used for this purpose are designed almost exclusively for perspective images. Applying algorithms designed for perspective images directly to wide-angle images is problematic as no account is made for the image distortion. The primary contribution of this thesis is the development of two novel keypoint detectors, and a method of keypoint description, designed for wide-angle images. Both reformulate the Scale- Invariant Feature Transform (SIFT) as an image processing operation on the sphere. As the image captured by any central projection wide-angle camera can be mapped to the sphere, applying these variants to an image on the sphere enables keypoints to be detected in a manner that is invariant to image distortion. Each of the variants is required to find the scale-space representation of an image on the sphere, and they differ in the approaches they used to do this. Extensive experiments using real and synthetically generated wide-angle images are used to validate the two new keypoint detectors and the method of keypoint description. The best of these two new keypoint detectors is applied to vision based localisation tasks including visual odometry and visual place recognition using outdoor wide-angle image sequences. As part of this work, the effect of keypoint coordinate selection on the accuracy of egomotion estimates using the Direct Linear Transform (DLT) is investigated, and a simple weighting scheme is proposed which attempts to account for the uncertainty of keypoint positions during detection. A word reliability metric is also developed for use within a visual ‘bag of words’ approach to place recognition.
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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 objects from the background in images.
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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 may still causes some false matches. To increase the number of keypoints, SIFT and FAST (Features from accelerated segment test) keypoints are combined for face image matching. Since there is no corresponding descriptor for FAST detector, the LOG (Laplace of Gaussian) function with Automatic Scale Selection is applied on each FAST keypoint to find proper scales and corresponding SIFT descriptors. On the other hand, based on the similarities between locations of features on human faces, three keypoint exclusion methods (relative location, orientation, and scale) are proposed to eliminate impossible keypoints for further descriptor matching. In this way, the number of false matches can be reduced and hence higher recognition rates can be obtained. On the other hand, matching time can also be reduced. The proposed algorithms are evaluated with the ORL and the Yale face databases. Each database pick 10 person, every person get 10 image. Our proposed method shows significantly improvements on recognition rates over conventional methods.
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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 of pixels but also color information. Experimental results show that the proposed feature descriptor spends fewer storage spaces while having better precision level than other popular binary descriptors. Besides, the proposed feature descriptor has the fastest matching speed among all the descriptors under comparison, which makes it suitable for real-time applications.
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Book chapters on the topic "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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Conference papers on the topic "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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