loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Paper Unlock
JOINT PRIOR MODELS OF MUMFORD-SHAH REGULARIZATION FOR BLUR IDENTIFICATION AND SEGMENTATION IN VIDEO SEQUENCES

Topics: Enhancement and Restoration; Image Filtering; Image Quality; Model-Based Object Tracking in Image Sequences; Pattern Recognition in Image Understanding; Segment Cluster Tracking; Segmentation and Grouping; Statistical Approach; Tracking of People and Surveillance; Video Analysis; Visual Learning

Authors: Hongwei Zheng and Olaf Hellwich

Affiliation: Computer Vision & Remote Sensing, Berlin University of Technology, Germany

ISBN: 972-8865-40-6

ISSN: 2184-4321

Keyword(s): Bayesian estimation, point spread function, blind image deconvolution, Mumford-Shah functional, partial differential equations, Γ-convergence, piecewise smooth approximate, graph-grouping, segmentation.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Enhancement and Restoration ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Image Filtering ; Image Formation and Preprocessing ; Image Quality ; Methodologies and Methods ; Model-Based Object Tracking in Image Sequences ; Motion, Tracking and Stereo Vision ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Segment Cluster Tracking ; Segmentation and Grouping ; Sensor Networks ; Soft Computing ; Software Engineering ; Statistical Approach ; Tracking of People and Surveillance ; Video Analysis

Abstract: We study a regularized Mumford-Shah functional in the context of joint prior models for blur identification, blind image deconvolution and segmentation. For the ill-posed regularization problem, it is hard to find a good initial value for ensuring the soundness of the convergent value. A newly introduced prior solution space of point spread functions in a double regularized Bayesian estimation can satisfy such demands. The Mumford-Shah functional is formulated using Γ-convergence approximation and is minimized by projecting iterations onto an alternating minimization within Neumann conditions. The pre-estimated priors support the Mumford-Shah functional to decrease of the complexity of computation and improve the restoration results simultaneously. Moreover, segmentation of blurred objects is more difficult. A graph-theoretic approach is used to group edges which driven from the Mumford-Shah functional. Blurred objects with lower gradients and objects with stronger gradients are grou ped separately. Numerical experiments show that the proposed algorithm is robust and efficiency in that it can handle images that are formed in different environments with different types and amounts of blur and noise. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.223.30

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Zheng, H. and Hellwich, O. (2006). JOINT PRIOR MODELS OF MUMFORD-SHAH REGULARIZATION FOR BLUR IDENTIFICATION AND SEGMENTATION IN VIDEO SEQUENCES. In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6; ISSN 2184-4321, pages 56-63. DOI: 10.5220/0001371400560063

@conference{visapp06,
author={Hongwei Zheng. and Olaf Hellwich.},
title={JOINT PRIOR MODELS OF MUMFORD-SHAH REGULARIZATION FOR BLUR IDENTIFICATION AND SEGMENTATION IN VIDEO SEQUENCES},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001371400560063},
isbn={972-8865-40-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - JOINT PRIOR MODELS OF MUMFORD-SHAH REGULARIZATION FOR BLUR IDENTIFICATION AND SEGMENTATION IN VIDEO SEQUENCES
SN - 972-8865-40-6
IS - 2184-4321
AU - Zheng, H.
AU - Hellwich, O.
PY - 2006
SP - 56
EP - 63
DO - 10.5220/0001371400560063

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.
0123movie.net