Relevant bibliographies by topics / Segmentation; Feature tracking; Computer vision

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

Academic literature on the topic 'Segmentation; Feature tracking; Computer vision'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Segmentation; Feature tracking; Computer vision"

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Kushwah, Chandra Pal. "Review on Semantic Segmentation of Satellite Images Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3820–29. http://dx.doi.org/10.22214/ijraset.2021.37204.

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Image segmentation for applications like scene understanding, medical image analysis, robotic vision, video tracking, improving reality, and image compression is a key subject of image processing and image evaluation. Semantic segmentation is an integral aspect of image comprehension and is essential for image processing tasks. Semantic segmentation is a complex process in computer vision applications. Many techniques have been developed, from self-sufficient cars, human interaction, robotics, medical science, agriculture, and so on, to tackle the issue.In a short period, satellite imagery wil
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KONWAR, LAKHYADEEP, ANJAN KUMAR TALUKDAR, and KANDARPA KUMAR SARMA. "Robust Real Time Multiple Human Detection and Tracking for Automatic Visual Surveillance System." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 17 (August 6, 2021): 93–98. http://dx.doi.org/10.37394/232014.2021.17.13.

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Detection of human for visual surveillance system provides most important rule for advancement in the design of future automation systems. Human detection and tracking are important for future automatic visual surveillance system (AVSS). In this paper we have proposed a flexible technique for proper human detection and tracking for the design of AVSS. We used graph cut for segment human as a foreground image by eliminating background, extract some feature points by using HOG, SVM classifier for proper classification and finally we used particle filter for tracking those of detected human. Our
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Zhang, Yiqing, Jun Chu, Lu Leng, and Jun Miao. "Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation." Sensors 20, no. 4 (2020): 1010. http://dx.doi.org/10.3390/s20041010.

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With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN
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Zhang, Xinyu, Hongbo Gao, Chong Xue, Jianhui Zhao, and Yuchao Liu. "Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters." International Journal of Advanced Robotic Systems 15, no. 1 (2018): 172988141774994. http://dx.doi.org/10.1177/1729881417749949.

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Intelligent transportation systems and safety driver-assistance systems are important research topics in the field of transportation and traffic management. This study investigates the key problems in front vehicle detection and tracking based on computer vision. A video of a driven vehicle on an urban structured road is used to predict the subsequent motion of the front vehicle. This study provides the following contributions. (1) A new adaptive threshold segmentation algorithm is presented in the image preprocessing phase. This algorithm is resistant to interference from complex environments
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Yao, Li Feng, and Jian Fei Ouyang. "Catching Data from Displayers by Machine Vision." Advanced Materials Research 566 (September 2012): 124–29. http://dx.doi.org/10.4028/www.scientific.net/amr.566.124.

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With the emergence of eHealth, the importance of keeping digital personal health statistics is quickly rising in demand. Many current health assessment devices output values to the user without a method of digitally saving the data. This paper presents a method to directly translate the numeric displays of the devices into digital records using machine vision. A wireless-based machine vision system is designed to image the display and a tracking algorithm based on SIFT (Scale Invariant Feature Transform) is developed to recognize the numerals from the captured images. First, a local camera cap
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Khalid, Nida, Munkhjargal Gochoo, Ahmad Jalal, and Kibum Kim. "Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System." Sustainability 13, no. 2 (2021): 970. http://dx.doi.org/10.3390/su13020970.

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Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra dept
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Et. al., Mohan kumar Shilpa ,. "An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 2506–14. http://dx.doi.org/10.17762/turcomat.v12i2.2098.

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Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characterist
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Kim, Byung-Gyu, and Dong-Jo Park. "Unsupervised video object segmentation and tracking based on new edge features." Pattern Recognition Letters 25, no. 15 (2004): 1731–42. http://dx.doi.org/10.1016/j.patrec.2004.07.009.

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Abdulghafoor, Nuha, and Hadeel Abdullah. "Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 533–45. http://dx.doi.org/10.22266/ijies2020.1231.47.

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Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the t
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Volkov, Vladimir Yu, Oleg A. Markelov, and Mikhail I. Bogachev. "IMAGE SEGMENTATION AND OBJECT SELECTION BASED ON MULTI-THRESHOLD PROCESSING." Journal of the Russian Universities. Radioelectronics 22, no. 3 (2019): 24–35. http://dx.doi.org/10.32603/1993-8985-2019-22-3-24-35.

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Introduction. Detection, isolation, selection and localization of variously shaped objects in images are essential in a variety of applications. Computer vision systems utilizing television and infrared cameras, synthetic aperture surveillance radars as well as laser and acoustic remote sensing systems are prominent examples. Such problems as object identification, tracking and matching as well as combining information from images available from different sources are essential. Objective. Design of image segmentation and object selection methods based on multi-threshold processing. Materials a
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Dissertations / Theses on the topic "Segmentation; Feature tracking; Computer vision"

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Wiles, Charles S. "Closing the loop on multiple motions." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320152.

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Graves, Alex. "GPU-Accelerated Feature Tracking." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1462372516.

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Möller, Sebastian. "Image Segmentation and Target Tracking using Computer Vision." Thesis, Linköpings universitet, Datorseende, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-68061.

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In this master thesis the possibility of detecting and tracking objects in multispectral infrared video sequences is investigated. The current method  with fix-sized rectangles have significant disadvantages. These disadvantages will be solved using image segmentation to estimate the shape of the object. The result of the image segmentation is used to determine the infrared contrast of the object. Our results show how some objects will give very good segmentation, tracking as well as shape detection. The objects that perform best are the flares and countermeasures. But especially helicopters s
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Rowe, Simon Michael. "Robust feature search for active tracking." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318616.

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Pretorius, Eugene. "An adaptive feature-based tracking system." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/1441.

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Lan, Xiangyuan. "Multi-cue visual tracking: feature learning and fusion." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/319.

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As an important and active research topic in computer vision community, visual tracking is a key component in many applications ranging from video surveillance and robotics to human computer. In this thesis, we propose new appearance models based on multiple visual cues and address several research issues in feature learning and fusion for visual tracking. Feature extraction and feature fusion are two key modules to construct the appearance model for the tracked target with multiple visual cues. Feature extraction aims to extract informative features for visual representation of the tracked ta
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Sun, Shijun. "Video object segmentation and tracking using VSnakes /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6038.

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Roberts, Jonathan Michael. "Attentive visual tracking and trajectory estimation for dynamic scene segmentation." Thesis, University of Southampton, 1994. https://eprints.soton.ac.uk/250163/.

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Intelligent Co-Pilot Systems (ICPS) offer the next challenge to vehicle-highway automation. The key to ICPSs is the detection of moving objects (other vehicles) from the moving observer using a visual sensor. The aim of the work presented in this thesis was to design and implement a feature detection and tracking strategy that is capable of tracking image features independently, in parallel, and in real-time and to cluster/segment features utilising the inherent temporal information contained within feature trajectories. Most images contain areas that are of little or no interest to vision tas
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Roychoudhury, Shoumik. "Tracking Human in Thermal Vision using Multi-feature Histogram." Master's thesis, Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/203794.

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Electrical Engineering<br>M.S.E.E.<br>This thesis presents a multi-feature histogram approach to track a person in thermal vision. Illumination variation is a primary constraint in the performance of object tracking in visible spectrum. Thermal infrared (IR) sensor, which measures the heat energy emitted from an object, is less sensitive to illumination variations. Therefore, thermal vision has immense advantage in object tracking in varying illumination conditions. Kernel based approaches such as mean shift tracking algorithm which uses a single feature histogram for object representation, ha
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Fang, Jian. "Optical Imaging and Computer Vision Technology for Corn Quality Measurement." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/733.

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The official U.S. standards for corn have been available for almost one hundred years. Corn grading system has been gradually updated over the years. In this thesis, we investigated a fast corn grading system, which includes the mechanical part and the computer recognition part. The mechanical system can deliver the corn kernels onto the display plate. For the computer recognition algorithms, we extracted common features from each corn kernel, and classified them to measure the grain quality.
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Books on the topic "Segmentation; Feature tracking; Computer vision"

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Video segmentation and its applications. Springer, 2011.

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Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2011.

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Ngan, King Ngi, and Hongliang Li. Video Segmentation and Its Applications. Springer, 2014.

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Book chapters on the topic "Segmentation; Feature tracking; Computer vision"

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Kwolek, Bogdan. "Foreground Segmentation via Segments Tracking." In Computer Vision and Graphics. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02345-3_27.

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Özuysal, Mustafa, Vincent Lepetit, François Fleuret, and Pascal Fua. "Feature Harvesting for Tracking-by-Detection." In Computer Vision – ECCV 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11744078_46.

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Makadia, Ameesh. "Feature Tracking for Wide-Baseline Image Retrieval." In Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15555-0_23.

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Southall, B., J. A. Marchant, T. Hague, and B. F. Buxton. "Model based tracking for navigation and segmentation." In Computer Vision — ECCV'98. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0055705.

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Raja, Yogesh, Stephen J. McKenna, and Shaogang Gong. "Segmentation and tracking using colour mixture models." In Computer Vision — ACCV'98. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63930-6_173.

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Zhang, Zhenli, Xiangyu Zhang, Chao Peng, Xiangyang Xue, and Jian Sun. "ExFuse: Enhancing Feature Fusion for Semantic Segmentation." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01249-6_17.

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Le, Hieu, Vu Nguyen, Chen-Ping Yu, and Dimitris Samaras. "Geodesic Distance Histogram Feature for Video Segmentation." In Computer Vision – ACCV 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54181-5_18.

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Gehrig, Daniel, Henri Rebecq, Guillermo Gallego, and Davide Scaramuzza. "Asynchronous, Photometric Feature Tracking Using Events and Frames." In Computer Vision – ECCV 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01258-8_46.

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Zheng, Linyu, Ming Tang, Yingying Chen, Jinqiao Wang, and Hanqing Lu. "Learning Feature Embeddings for Discriminant Model Based Tracking." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58555-6_45.

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Alismail, Hatem, Brett Browning, and Simon Lucey. "Enhancing Direct Camera Tracking with Dense Feature Descriptors." In Computer Vision – ACCV 2016. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54190-7_33.

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Conference papers on the topic "Segmentation; Feature tracking; Computer vision"

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Allili, Mohand Said, and Djemel Ziou. "Using Feature Selection For Object Segmentation and Tracking." In >Fourth Canadian Conference on Computer and Robot Vision. IEEE, 2007. http://dx.doi.org/10.1109/crv.2007.67.

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Sun, Chuan, Marshall Tappen, and Hassan Foroosh. "Feature-Independent Action Spotting without Human Localization, Segmentation, or Frame-wise Tracking." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.344.

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Allili, Mohand Said, and Djemel Ziou. "Object of Interest segmentation and Tracking by Using Feature Selection and Active Contours." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383449.

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Ring, Dan, and Anil Kokaram. "Feature-Cut: Video object segmentation through local feature correspondences." In 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccvw.2009.5457644.

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Brendel, William, and Sinisa Todorovic. "Video object segmentation by tracking regions." In 2009 IEEE 12th International Conference on Computer Vision (ICCV). IEEE, 2009. http://dx.doi.org/10.1109/iccv.2009.5459242.

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Ren, Xiaofeng, and Jitendra Malik. "Tracking as Repeated Figure/Ground Segmentation." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383177.

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Ding, Henghui, Xudong Jiang, Ai Qun Liu, Nadia Magnenat Thalmann, and Gang Wang. "Boundary-Aware Feature Propagation for Scene Segmentation." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00692.

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Ikedo, Ryota, and Kazuhiro Hotta. "Feature Sharing Cooperative Network for Semantic Segmentation." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010312505770584.

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Allili, Mohand Saïd, Djemel Ziou, Nizar Bouguila, and Sabri Boutemedjet. "Unsupervised Feature Selection and Learning for Image Segmentation." In 2010 Canadian Conference on Computer and Robot Vision. IEEE, 2010. http://dx.doi.org/10.1109/crv.2010.44.

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"Sparse Motion Segmentation using Propagation of Feature Labels." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004281203960401.

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Journal articles Dissertations / Theses
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