Information Sciences: an International Journal
Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Rough Fuzzy Image Analysis: Foundations and Methodologies
Rough Fuzzy Image Analysis: Foundations and Methodologies
International Journal of Bio-Inspired Computation
Tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Corrigenda and addenda: tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Parallel computation in finding near neighbourhoods
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Tolerance spaces: Origins, theoretical aspects and applications
Information Sciences: an International Journal
Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis
Transactions on Rough Sets XV
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Hi-index | 0.00 |
The problem considered in this article is how to measure the nearness or apartness of digital images in cases where it is important to detect subtle changes in the contour, position, and spatial orientation of bounded regions. The solution of this problem results from an application of anisotropic (direction dependent) wavelets and a tolerance near set approach to detecting similarities in pairs of images. A wavelet-based tolerance Nearness Measure (tNM) makes it possible to measure fine-grained differences in shapes in pairs of images. The application of the proposed method focuses on image sequences extracted from hand-finger movement videos. Each image sequence consists of hand-finger movements recorded during rehabilitation exercises. The nearness of pairs of images from such sequences is measured to check the extent that normal hand-finger movement differs from arthritic hand-finger movement. Experimental results of the proposed approach are reported, here. The contribution of this article is an application of an anisotropic wavelet-based tNM in classifying arthritic hand-finger movement images in terms of their degree of nearness to or apartness from normal hand-finger movement images.