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Thèses sur le sujet « Keypoint-based »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

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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Tvoroshenko, I., and O. Babochkin. "Object identification method based on image keypoint descriptors." Thesis, 2021. https://openarchive.nure.ua/handle/document/16193.

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Syu, Jhih-Wei, and 許智維. "A Keypoint Detector Based on Local Contrast Intensity Images." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/24007133216259018431.

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碩士<br>逢甲大學<br>通訊工程所<br>98<br>Corners, junctions, and terminals represent prominent local features in images. They are named keypoints. Keypoint detection is a vital step in many applications such as pattern recognition and image registration. The purpose of this thesis is to develop a keypoint detector based on local contrast intensity. Initially an input image is enhanced by a compressive mapping curve and then is transformed to a line-type image by computing absolute local contrast. Subsequently, the local contrast intensity image is applied to the multi-scale and multi-orientation Gaussian

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Chen, Kuan-Yu, and 陳冠宇. "Keypoint Selection for Bag-of-Words Based Image Classification." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/72145970597038218165.

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碩士<br>國立中央大學<br>資訊管理研究所<br>100<br>To search images from large image databases, image retrieval is the major technique to retrieve similar images based on users’ queries. In order to allow users to provide keyword-based queries, automatically annotating images with keywords has been extensively studied. In particular, BOW(Bag Of Words)and SPM(Spatial Pyramid Matching Bag Of Words) are two well-known methods to represent image content as the image feature descriptors. To extract the BOW or SPM features, some keypoints must be detected from each image. However, the number of the detected keypoint

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Chen, Ting-Kai, and 陳定楷. "Laser-Based SLAM Using Segmenting Keypoint Detection and B-SHOT Feature." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/53hp8q.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>106<br>Simultaneous localization and mapping is a basic and essential part of the autonomous driving research. Environment information gathered from sensors is computed and derives a consistent state of both self-driving car and the environment. Many types of sensor have been utilized in SLAM research, including camera and LiDAR. LiDAR can provide precise depth information, but suffers from the sparsity compared to camera images. Two main methods have been used in LiDAR-based SLAM: direct method and modeling after segmentation. Direct method first extracts interesti

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Chang, Keng-Hao, and 張耿豪. "6-DoF Object Pose Estimation Using Keypoint-based Segmentation in Occluded Environment." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7rd758.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>107<br>Object pose estimation has been an important research topic due to its various applications such as robot manipulation and augmented reality. In real-world applications, speed requirement and occlusion handling are two difficulties often addressed. Due to the high price of depth cameras and the lack of research on depth cameras, traditional pose estimation methods emphasize using color images without depth information. With the development of depth cameras, this thesis proposes a 6-DoF object pose estimation method using RGB-D images. Inspired by bottom-up hu

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