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Journal of Flow Visualization and Image Processing
Главный редактор: Krishnamurthy Muralidhar (open in a new tab)

Выходит 4 номеров в год

ISSN Печать: 1065-3090

ISSN Онлайн: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

VISUAL TRACKING WITH METHODOLOGIES − A LITERATURE SURVEY

Том 23, Выпуск 3-4, 2016, pp. 275-321
DOI: 10.1615/JFlowVisImageProc.2017020118
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Краткое описание

Various algorithms used in computer vision are discussed here. The Kalman filter widely used in various computer vision applications includes object tracking, pattern recognition, etc. The Kalman filter was further improved to an extended Kalman filter with some additional features cited in this paper. Subsequently, the Particle filter as a filtering algorithm was developed with some distinct features. However, like Kalman filters, they opened a great way for tracking the state of a dynamic system for which one has a Bayesian model. The Particle filter was further modified to an improved particle filtering tracking algorithm for focusing on the problem of tracking a moving object in image sequences with complex background. Moreover, for somewhat similar type of applications, Optical flow was introduced. The term "Optical flow" is also used by roboticists, encompassing related techniques from image processing and control of navigation including motion detection, object segmentation, time-to-contact information, focus of expansion calculations, luminance, motion- compensated encoding, and stereo disparity measurement. It is very helpful for 2D motion and tracking. Considering Optical flow as a base, another flow technique known as Scene flow/Scene flow estimation is devised for 3D motion and tracking that is very helpful in state-of-the-art Visual Odometry and SLAM algorithms. This paper is a critical review/survey on the existing literature relevant to computer vision algorithms and their applications. Emphasis has been put on succinctly providing the knowledge regarding all previous and latest algorithms implemented in the field of computer vision.

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