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Authors: Dubravko Culibrk ; Oge Marques ; Daniel Socek ; Hari Kalva and Borko Furht

Affiliation: Florida Atlantic University, United States

ISBN: 972-8865-40-6

Keyword(s): Video processing, Object segmentation, Background modeling, Bayesian modeling, Neural Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Segmentation and Grouping ; Sensor Networks ; Signal Processing ; Soft Computing ; Statistical Approach ; Theory and Methods

Abstract: Object segmentation from a video stream is an essential task in video processing and forms the foundation of scene understanding, object-based video encoding (e.g. MPEG4), and various surveillance and2D-to-pseudo-3D conversion applications. The task is difficult and exacerbated by the advances in video capture and storage. Increased resolution of the sequences requires development of new, more efficient algorithms for object detection and segmentation. The paper presents a novel neural network based approach to background modeling for motion based object segmentation in video sequences. The proposed approach is designed to enable efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real time segmentation of high-resolution sequences.

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Paper citation in several formats:
Culibrk D.; Marques O.; Socek D.; Kalva H.; Furht B. and (2006). A NEURAL NETWORK APPROACH TO BAYESIAN BACKGROUND MODELING FOR VIDEO OBJECT SEGMENTATION.In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 474-479. DOI: 10.5220/0001374604740479

@conference{visapp06,
author={Dubravko Culibrk and Oge Marques and Daniel Socek and Hari Kalva and Borko Furht},
title={A NEURAL NETWORK APPROACH TO BAYESIAN BACKGROUND MODELING FOR VIDEO OBJECT SEGMENTATION},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={474-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001374604740479},
isbn={972-8865-40-6},
}

TY - CONF

JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - A NEURAL NETWORK APPROACH TO BAYESIAN BACKGROUND MODELING FOR VIDEO OBJECT SEGMENTATION
SN - 972-8865-40-6
AU - Culibrk, D.
AU - Marques, O.
AU - Socek, D.
AU - Kalva, H.
AU - Furht, B.
PY - 2006
SP - 474
EP - 479
DO - 10.5220/0001374604740479

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