A physics-inspired model of image structure representation by deformable elastic grid

Authors:
X. D. Zhuang;N. E. Mastorakis
Affiliations:
WSEAS Headquarters, Zografou, Athens, Greece;WSEAS Headquarters, Zografou, Athens, Greece and Department of Computer Science, Military Institutions of University Education, Hellenic Naval Academy, Piraeus, Greece
Venue:
CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2008

Citing 6
Cited 0

Physics-based segmentation of complex objects using multiple hypotheses of image formation

Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Shape Representation Using a Generalized Potential Field Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
Corner detection via topographic analysis of vector-potential

Pattern Recognition Letters
Scale-Space Vector Fields for Feature Analysis

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Force field feature extraction for ear biometrics

Computer Vision and Image Understanding
The curling vector field transform of gray-scale images: a magneto-static inspired approach

WSEAS Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel physics-inspired model is proposed for image structure representation. The deformable elastic grid is defined on digital images. The attracting force between adjacent points is defined according to the gray-scale difference, which is the source of force causing the deformation of the elastic grid. The final shape of the grid after deformation can represent image structure information, based on which a segmentation method is proposed for digital images. Experimental results indicate the effectiveness of the proposed method.