Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Corrigenda and addenda: tolerance near sets and image correspondence
International Journal of Bio-Inspired Computation
Perceptually near pawlak partitions
Transactions on rough sets XII
Perceptual tolerance intersection
Transactions on rough sets XIII
Spatiotemporal approach for tracking using rough entropy and frame subtraction
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Nature-inspired framework for measuring visual image resemblance: A near rough set approach
Theoretical Computer Science
Nearness of subtly different digital images
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
Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
Nearness of subtly different digital images
Transactions on Rough Sets XVI
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Fuzzy sets, near sets, and rough sets are useful and important stepping stones in a variety of approaches to image analysis. These three types of sets and their various hybridizations provide powerful frameworks for image analysis. Emphasizing the utility of fuzzy, near, and rough sets in image analysis, Rough Fuzzy Image Analysis: Foundations and Methodologies introduces the fundamentals and applications in the state of the art of rough fuzzy image analysis. In the first chapter, the distinguished editors explain how fuzzy, near, and rough sets provide the basis for the stages of pictorial pattern recognition: image transformation, feature extraction, and classification. The text then discusses hybrid approaches that combine fuzzy sets and rough sets in image analysis, illustrates how to perform image analysis using only rough sets, and describes tolerance spaces and a perceptual systems approach to image analysis. It also presents a free, downloadable implementation of near sets using the Near Set Evaluation and Recognition (NEAR) system, which visualizes concepts from near set theory. In addition, the book covers an array of applications, particularly in medical imaging involving breast cancer diagnosis, laryngeal pathology diagnosis, and brain MR segmentation. Edited by two leading researchers and with contributions from some of the best in the field, this volume fully reflects the diversity and richness of rough fuzzy image analysis. It deftly examines the underlying set theories as well as the diverse methods and applications.