Please use this identifier to cite or link to this item:
|Title:||Advanced statistical and adaptive threshold techniques for moving object detection and segmentation||Authors:||Marques, Oge
|Keywords:||Computer vision;Statistics;Standard deviations;Computer graphics||Category:||Electrical Engineering - Electronic Engineering - Information Engineering||Field:||Engineering and Technology||Issue Date:||30-Aug-2011||Publisher:||IEEE||Source:||17th International Conference on Digital Signal Processing, 2011, Pages 1-6||Conference:||International Conference on Digital Signal Processing||Abstract:||The current research project proposes advanced statistical and adaptive threshold techniques for video object detection and segmentation. We present new statistical adaptive threshold techniques to show the advantages, and how these algorithms overcome the limitations and the technical challenges for object motion detection. The algorithm utilizes statistical quantities such as mean, standard deviation, and variance to define a new adaptive and automatic threshold based on two-frame and three-frame differencing. The proposed algorithms were compared with classic statistical thresholding methods on a testing video for human motion detection, and the experimental results show the effectiveness of the algorithms. Furthermore this research shows an evaluation and comparison among all statistical and adaptive algorithms and proves the benefits of the proposed algorithm.||URI:||http://ktisis.cut.ac.cy/handle/10488/7003||ISBN:||978-1-4577-0274-7||ISSN:||2165-3577||DOI:||10.1109/ICDSP.2011.6004875||Rights:||© 2011 IEEE||Type:||Conference Papers|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
Show full item record
checked on Apr 28, 2018
checked on Aug 17, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.