Fundamentals of digital image processing
Fundamentals of digital image processing
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
Standard and Genetic k-means Clustering Techniques in Image Segmentation
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
Rough Granular Computing in Knowledge Discovery and Data Mining
Rough Granular Computing in Knowledge Discovery and Data Mining
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Rough Entropy Based k-Means Clustering
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Probabilistic rough entropy measures in image segmentation
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Perceptually near pawlak partitions
Transactions on rough sets XII
The Knowledge Engineering Review
Rough entropy hierarchical agglomerative clustering in image segmentation
Transactions on rough sets XIII
Subspace entropy maps for rough extended framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
RECA components in rough extended clustering framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Uniform RECA transformations in rough extended clustering framework
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Perceptual indiscernibility, rough sets, descriptively near sets, and image analysis
Transactions on Rough Sets XV
Can fuzzy entropies be effective measures for evaluating the roughness of a rough set?
Information Sciences: an International Journal
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Clustering or data grouping presents fundamental initial procedure in image processing. This paper addresses the problem of combining the concept of rough sets and entropy measure in the area of image segmentation. In the present study, comprehensive investigation into rough set entropy based thresholding image segmentation techniques has been performed. Segmentation presents the low-level image transformation routine concerned with image partitioning into distinct disjoint and homogenous regions with thresholding algorithms most often applied in practical solutions when there is pressing need for simplicity and robustness. Simultaneous combining entropy based thresholding with rough sets results in rough entropy thresholding algorithm. In the present paper, new algorithmic schemes Standard RECA(Rough Entropy Clustering Algorithm) and Fuzzy RECAin the area of rough entropy based partitioning routines have been proposed. Rough entropy clustering incorporates the notion of rough entropy into clustering model taking advantage of dealing with some degree of uncertainty in analyzed data. Both Standard and Fuzzy RECAalgorithmic schemes performed usually equally robustly compared to standard k-means algorithm. At the same time, in many runs yielding slightly better performance making possible future implementation in clustering applications.