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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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Chen, Dong, Fan Tang, Weiming Dong, Hanxing Yao, and Changsheng Xu. "SiamCPN: Visual tracking with the Siamese center-prediction network." Computational Visual Media 7, no. 2 (2021): 253–65. http://dx.doi.org/10.1007/s41095-021-0212-1.

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AbstractObject detection is widely used in object tracking; anchor-free object tracking provides an end-to-end single-object-tracking approach. In this study, we propose a new anchor-free network, the Siamese center-prediction network (SiamCPN). Given the presence of referenced object features in the initial frame, we directly predict the center point and size of the object in subsequent frames in a Siamese-structure network without the need for perframe post-processing operations. Unlike other anchor-free tracking approaches that are based on semantic segmentation and achieve anchor-free trac

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Reddy Gurunatha Swamy, P., and B. Ananth Reddy. "Human Pose Estimation in Images and Videos." International Journal of Engineering & Technology 7, no. 3.27 (2018): 1. http://dx.doi.org/10.14419/ijet.v7i3.27.17640.

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Estimation of human poses is an interesting and challenging topic in the field of Computer vision. It includes some un-noticed challenges like background effect, the color of the dress, skin tones and many other unpredictable challenges. This is a workable concept because it can be used in sign language recognition, correlating various pose styles from different parts of the world and in medical applications. A deep structure which can represent a man’s body in different models will help in improved recognition of body parts and the spatial correlation between them. For hand detection, feature

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Shariff, Aabid, Joshua Kangas, Luis Pedro Coelho, Shannon Quinn, and Robert F. Murphy. "Automated Image Analysis for High-Content Screening and Analysis." Journal of Biomolecular Screening 15, no. 7 (2010): 726–34. http://dx.doi.org/10.1177/1087057110370894.

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The field of high-content screening and analysis consists of a set of methodologies for automated discovery in cell biology and drug development using large amounts of image data. In most cases, imaging is carried out by automated microscopes, often assisted by automated liquid handling and cell culture. Image processing, computer vision, and machine learning are used to automatically process high-dimensional image data into meaningful cell biological results. The key is creating automated analysis pipelines typically consisting of 4 basic steps: (1) image processing (normalization, segmentati

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Feng, Zhanshen. "An Image Detection Method Based on Parameter Optimization of Support Vector Machine." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 306–14. http://dx.doi.org/10.46300/9106.2021.15.35.

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With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, sem

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Prahara, Adhi, Murinto Murinto, and Dewi Pramudi Ismi. "Bottom-up visual attention model for still image: a preliminary study." International Journal of Advances in Intelligent Informatics 6, no. 1 (2020): 82. http://dx.doi.org/10.26555/ijain.v6i1.469.

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The philosophy of human visual attention is scientifically explained in the field of cognitive psychology and neuroscience then computationally modeled in the field of computer science and engineering. Visual attention models have been applied in computer vision systems such as object detection, object recognition, image segmentation, image and video compression, action recognition, visual tracking, and so on. This work studies bottom-up visual attention, namely human fixation prediction and salient object detection models. The preliminary study briefly covers from the biological perspective o

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RAUTARAY, SIDDHARTH S., and ANUPAM AGRAWAL. "VISION-BASED APPLICATION-ADAPTIVE HAND GESTURE RECOGNITION SYSTEM." International Journal of Information Acquisition 09, no. 01 (2013): 1350007. http://dx.doi.org/10.1142/s0219878913500071.

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With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands

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Ha, In Young, Matthias Wilms, and Mattias Heinrich. "Semantically Guided Large Deformation Estimation with Deep Networks." Sensors 20, no. 5 (2020): 1392. http://dx.doi.org/10.3390/s20051392.

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Deformable image registration is still a challenge when the considered images have strong variations in appearance and large initial misalignment. A huge performance gap currently remains for fast-moving regions in videos or strong deformations of natural objects. We present a new semantically guided and two-step deep deformation network that is particularly well suited for the estimation of large deformations. We combine a U-Net architecture that is weakly supervised with segmentation information to extract semantically meaningful features with multiple stages of nonrigid spatial transformer

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Wu, Bing-Fei, Chih-Chung Kao, Ying-Feng Li, and Min-Yu Tsai. "A Real-Time Embedded Blind Spot Safety Assistance System." International Journal of Vehicular Technology 2012 (April 22, 2012): 1–15. http://dx.doi.org/10.1155/2012/506235.

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This paper presents an effective vehicle and motorcycle detection system in the blind spot area in the daytime and nighttime scenes. The proposed method identifies vehicle and motorcycle by detecting the shadow and the edge features in the daytime, and the vehicle and motorcycle could be detected through locating the headlights at nighttime. First, shadow segmentation is performed to briefly locate the position of the vehicle. Then, the vertical and horizontal edges are utilized to verify the existence of the vehicle. After that, tracking procedure is operated to track the same vehicle in the

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King, A. P., P. J. Edwards, C. R. Maurer, et al. "Stereo Augmented Reality in the Surgical Microscope." Presence: Teleoperators and Virtual Environments 9, no. 4 (2000): 360–68. http://dx.doi.org/10.1162/105474600566862.

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This paper describes the MAGI (microscope-assisted guided interventions) augmented-reality system, which allows surgeons to view virtual features segmented from preoperative radiological images accurately overlaid in stereo in the optical path of a surgical microscope. The aim of the system is to enable the surgeon to see in the correct 3-D position the structures that are beneath the physical surface. The technical challenges involved are calibration, segmentation, registration, tracking, and visualization. This paper details our solutions to these problems. As it is difficult to make reliabl

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Ghose, Tandra, Yannik Schelske, Takeshi Suzuki, and Andreas Dengel. "Low-level pixelated representations suffice for aesthetically pleasing contrast adjustment in photographs." Psihologija 50, no. 3 (2017): 239–70. http://dx.doi.org/10.2298/psi1703239g.

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Today?s web-based automatic image enhancement algorithms decide to apply an enhancement operation by searching for ?similar? images in an online database of images and then applying the same level of enhancement as the image in the database. Two key bottlenecks in these systems are the storage cost for images and the cost of the search. Based on the principles of computational aesthetics, we consider storing task-relevant aesthetic summaries, a set of features which are sufficient to predict the level at which an image enhancement operation should be performed, instead of the entire image. The

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Watt, R. J. "Feature-based image segmentation in human vision." Spatial Vision 1, no. 3 (1986): 243–56. http://dx.doi.org/10.1163/156856886x00043.

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Dorini, Leyza Baldo, and Siome Klein Goldenstein. "Unscented feature tracking." Computer Vision and Image Understanding 115, no. 1 (2011): 8–15. http://dx.doi.org/10.1016/j.cviu.2010.07.009.

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Shih, Frank Y., and Xin Zhong. "Automated Counting and Tracking of Vehicles." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 12 (2017): 1750038. http://dx.doi.org/10.1142/s0218001417500380.

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A robust traffic surveillance system is crucial in improving the control and management of traffic systems. Vehicle flow processing primarily involves counting and tracking vehicles; however, due to complex situations such as brightness changes and vehicle partial occlusions, traditional image segmentation methods are unable to segment and count vehicles correctly. This paper presents a novel framework for vision-based vehicle counting and tracking, which consists of four main procedures: foreground detection, feature extraction, feature analysis, and vehicles counting/tracking. Foreground det

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Dankers, Andrew, Nick Barnes, and Alex Zelinsky. "MAP ZDF segmentation and tracking using active stereo vision: Hand tracking case study." Computer Vision and Image Understanding 108, no. 1-2 (2007): 74–86. http://dx.doi.org/10.1016/j.cviu.2006.10.013.

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Poling, Bryan, and Gilad Lerman. "Enhancing feature tracking with gyro regularization." Image and Vision Computing 50 (June 2016): 42–58. http://dx.doi.org/10.1016/j.imavis.2016.01.004.

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Harmanci, Yunus Emre, Zhilu Lai, Utku Gülan, Markus Holzner, and Eleni Chatzi. "Computer Vision Aided Structural Identification: Feature Tracking Using Particle Tracking Velocimetry versus Optical Flow." Proceedings 4, no. 1 (2018): 33. http://dx.doi.org/10.3390/ecsa-5-05750.

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Recent advances in computer vision techniques allow to obtain information on the dynamic behaviour of structures using commercial grade video recording devices. The advantage of such schemes lies in the non-invasive nature of video recording and the ability to extract information at a high spatial density utilizing structural features. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm

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Zhang, Peng, Tao Zhuo, Lei Xie, and Yanning Zhang. "Deformable object tracking with spatiotemporal segmentation in big vision surveillance." Neurocomputing 204 (September 2016): 87–96. http://dx.doi.org/10.1016/j.neucom.2015.07.149.

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Reid, Ian, and Keith Connor. "Multiview segmentation and tracking of dynamic occluding layers." Image and Vision Computing 28, no. 6 (2010): 1022–30. http://dx.doi.org/10.1016/j.imavis.2009.09.007.

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Heber, Markus, Martin Godec, Matthias Rüther, Peter M. Roth, and Horst Bischof. "Segmentation-based tracking by support fusion." Computer Vision and Image Understanding 117, no. 6 (2013): 573–86. http://dx.doi.org/10.1016/j.cviu.2013.02.001.

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Xu, Binbin, Andrew J. Davison, and Stefan Leutenegger. "Deep Probabilistic Feature-Metric Tracking." IEEE Robotics and Automation Letters 6, no. 1 (2021): 223–30. http://dx.doi.org/10.1109/lra.2020.3039216.

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Li, Qianwen, Zhihua Wei, and Wen Shen. "Selective Feature Fusion Based Adaptive Image Segmentation Algorithm." Advances in Multimedia 2018 (September 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/4724078.

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Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on differen

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Huerta, Ivan, Michael B. Holte, Thomas B. Moeslund, and Jordi Gonzàlez. "Chromatic shadow detection and tracking for moving foreground segmentation." Image and Vision Computing 41 (September 2015): 42–53. http://dx.doi.org/10.1016/j.imavis.2015.06.003.

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Nichol, David, and Merrilyn Fiebig. "Tracking multiple moving objects by binary object forest segmentation." Image and Vision Computing 9, no. 6 (1991): 362–71. http://dx.doi.org/10.1016/0262-8856(91)90003-8.

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Fusiello, A., E. Trucco, T. Tommasini, and V. Roberto. "Improving Feature Tracking with Robust Statistics." Pattern Analysis & Applications 2, no. 4 (1999): 312–20. http://dx.doi.org/10.1007/s100440050039.

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Lomakina-Rumyantseva, E., P. Voronin, D. Kropotov, D. Vetrov, and A. Konushin. "Video tracking and behaviour segmentation of laboratory rodents." Pattern Recognition and Image Analysis 19, no. 4 (2009): 616–22. http://dx.doi.org/10.1134/s1054661809040075.

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Takada, Chika, and Yasuyuki Sugaya. "Incorrect Feature Tracking Detection by Affine Space Fitting." IPSJ Transactions on Computer Vision and Applications 1 (2009): 174–82. http://dx.doi.org/10.2197/ipsjtcva.1.174.

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Wang, Zelun, Jinjun Wang, Shun Zhang, and Yihong Gong. "Visual tracking based on online sparse feature learning." Image and Vision Computing 38 (June 2015): 24–32. http://dx.doi.org/10.1016/j.imavis.2015.04.005.

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du Buf, J. M. H., M. Kardan, and M. Spann. "Texture feature performance for image segmentation." Pattern Recognition 23, no. 3-4 (1990): 291–309. http://dx.doi.org/10.1016/0031-3203(90)90017-f.

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Chu, Tao, Wenjie Cai, and Qiong Liu. "Learning panoptic segmentation through feature discriminability." Pattern Recognition 122 (February 2022): 108240. http://dx.doi.org/10.1016/j.patcog.2021.108240.

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Konwar, Lakhyadeep, Anjan Kumar Talukdar, Kandarpa Kumar Sarma, Navajit Saikia, and Subhash Chandra Rajbangshi. "Segmentation and Selective Feature Extraction for Human Detection to the Direction of Action Recognition." International Journal of Circuits, Systems and Signal Processing 15 (September 8, 2021): 1371–86. http://dx.doi.org/10.46300/9106.2021.15.147.

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Detection as well as classification of different object for machine vision application is a challenging task. Similar to the other object detection and classification task, human detection concept provides a major role for the ad- vancement in the design of an automatic visual surveillance system (AVSS). For the future automation system if it is possible to include human detection and tracking, human action recognition, usual as well as unusual event recognition etc. concept for future AVSS, it will be a greater success in the transformable world. In this paper we have proposed a proper human

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Zhong, Bineng, Shengnan Pan, Cheng Wang, et al. "Robust Individual-Cell/Object Tracking via PCANet Deep Network in Biomedicine and Computer Vision." BioMed Research International 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/8182416.

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Tracking individual-cell/object over time is important in understanding drug treatment effects on cancer cells and video surveillance. A fundamental problem of individual-cell/object tracking is to simultaneously address the cell/object appearance variations caused by intrinsic and extrinsic factors. In this paper, inspired by the architecture of deep learning, we propose a robust feature learning method for constructing discriminative appearance models without large-scale pretraining. Specifically, in the initial frames, an unsupervised method is firstly used to learn the abstract feature of

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McFarlane, N. J. B., and C. P. Schofield. "Segmentation and tracking of piglets in images." Machine Vision and Applications 8, no. 3 (1995): 187–93. http://dx.doi.org/10.1007/bf01215814.

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Li, Houjie, Shuangshuang Yin, Fuming Sun, and Fasheng Wang. "Face Tracking via Content Aware Correlation Filter." International Journal of Circuits, Systems and Signal Processing 15 (July 20, 2021): 677–89. http://dx.doi.org/10.46300/9106.2021.15.76.

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Face tracking is an importance task in many computer vision based augment reality systems. Correlation filters (CFs) have been applied with great success to several computer vision problems including object detection, classification and tracking, but few CF-based methods are proposed for face tracking. As an essential research direction in computer vision, face tracking is very important in many human-computer applications. In this paper, we present a content aware CF for face tracking. In our work, face content refers to the locality sensitive histogram based foreground feature and the learni

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Han, J., G. Awad, and A. Sutherland. "Automatic skin segmentation and tracking in sign language recognition." IET Computer Vision 3, no. 1 (2009): 24. http://dx.doi.org/10.1049/iet-cvi:20080006.

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Gao, Terry. "Detection and Tracking Cows by Computer Vision and Image Classification Methods." International Journal of Security and Privacy in Pervasive Computing 13, no. 1 (2021): 1–45. http://dx.doi.org/10.4018/ijsppc.2021010101.

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In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is tr

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Zhang, Tiedong, Shuwei Liu, Xiao He, Hai Huang, and Kangda Hao. "Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles." Sensors 20, no. 1 (2019): 102. http://dx.doi.org/10.3390/s20010102.

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In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in clutter

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Li, Ruoxiang, Dianxi Shi, Yongjun Zhang, Ruihao Li, and Mingkun Wang. "Asynchronous event feature generation and tracking based on gradient descriptor for event cameras." International Journal of Advanced Robotic Systems 18, no. 4 (2021): 172988142110270. http://dx.doi.org/10.1177/17298814211027028.

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Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an eve

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Umeda, Takayuki, Kosuke Sekiyama, and Toshio Fukuda. "Vision-Based Object Tracking by Multi-Robots." Journal of Robotics and Mechatronics 24, no. 3 (2012): 531–39. http://dx.doi.org/10.20965/jrm.2012.p0531.

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This paper proposes a cooperative visual object tracking by a multi-robot system, where robust cognitive sharing is essential between robots. Robots identify the object of interest by using various types of information in the image recognition field. However, the most effective type of information for recognizing an object accurately is the difference between the object and its surrounding environment. Therefore we propose two evaluation criteria, called ambiguity and stationarity, in order to select the best information. Although robots attempt to select the best available feature for recogni

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Schnorr, Andrea, Dirk N. Helmrich, Dominik Denker, Torsten W. Kuhlen, and Bernd Hentschel. "Feature Tracking by Two-Step Optimization." IEEE Transactions on Visualization and Computer Graphics 26, no. 6 (2020): 2219–33. http://dx.doi.org/10.1109/tvcg.2018.2883630.

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Katz, Sagi, George Leifman, and Ayellet Tal. "Mesh segmentation using feature point and core extraction." Visual Computer 21, no. 8-10 (2005): 649–58. http://dx.doi.org/10.1007/s00371-005-0344-9.

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