Shape Representation Using a Generalized Potential Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fourier analysis and applications: filtering, numerical computation, wavelets
Fourier analysis and applications: filtering, numerical computation, wavelets
Corner detection via topographic analysis of vector-potential
Pattern Recognition Letters
3D part segmentation: a new physics-based approach
ISCV '95 Proceedings of the International Symposium on Computer Vision
Combining Local and Global Image Features for Object Class Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Flexible Spatial Configuration of Local Image Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Field Model for Integration of Local Information and Global Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
The curling vector field transform of gray-scale images: a magneto-static inspired approach
WSEAS Transactions on Computers
Physics of Classical Electromagnetism
Physics of Classical Electromagnetism
A magneto-statics inspired transform for structure representation and analysis of digital images
WSEAS Transactions on Computers
Force field feature extraction for ear biometrics
Computer Vision and Image Understanding
Automatic discovery of image families: global vs. local features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper, the relative potential field is proposed as a novel image transform inspired by the physical electro-static field. A general form of image potential field is presented, based on which the relative potential is defined by introducing the factor of gray-scale difference into the potential field. The properties of the relative potential are investigated experimentally and analyzed, based on which an image segmentation method is proposed by region division and merging in the relative potential field. Experimental results prove the effectiveness of the proposed image segmentation method, and also indicate the promising application of the relative potential in image processing tasks.