Incomplete Information: Rough Set Analysis
Incomplete Information: Rough Set Analysis
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Information Sciences: an International Journal
Information Sciences: an International Journal
Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Tolerance Classes in Measuring Image Resemblance
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
International Journal of Bio-Inspired Computation
Corrigenda and addenda: tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Perceptually near pawlak partitions
Transactions on rough sets XII
Core-generating discretization for rough set feature selection
Transactions on rough sets XIII
Parallel computation in finding near neighbourhoods
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Nearness of subtly different digital images
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis
Transactions on Rough Sets XV
Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Development of Near Sets Within the Framework of Axiomatic Fuzzy Sets
Fundamenta Informaticae
Nearness of subtly different digital images
Transactions on Rough Sets XVI
Maximal clique enumeration in finding near neighbourhoods
Transactions on Rough Sets XVI
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The principal problem considered in this paper is how to solve the image correspondence problem using a bio-inspired approach. One solution to this problem is to consider tolerance near sets that model human perception in a physical continuum. Near sets are generalisations of rough sets introduced by Zdzislaw Pawlak during the early 1980s. Tolerance near sets have been inspired by C.E. Zeeman's work on visual perception and Henri Poincare's work on the contrast between mathematical continua and the physical continua in a pragmatic philosophy of science that laid the foundations for tolerance spaces. In this paper, the basics of perceptual systems and tolerance near sets are presented as bases for the solution of the image correspondence problem. The contribution of this paper is a humanistic perception-based approach to discovering similarities between images, classifying images and an approach to quantifying the nearness of images using the Henry-Peters nearness measure.