A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Vector quantization and signal compression
Vector quantization and signal compression
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Objective and quantitative segmentation evaluation and comparison
Signal Processing
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Digital Image Processing
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Segmentation of Medical Images Based on Approximations in Rough Set Theory
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
Modelling Complex Patterns by Information Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Reinforcement Learning with Approximation Spaces
Fundamenta Informaticae
Fast learning in networks of locally-tuned processing units
Neural Computation
Rough sets and information granulation
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Rough validity, confidence, and coverage of rules in approximation spaces
Transactions on Rough Sets III
Approximation spaces and information granulation
Transactions on Rough Sets III
IEEE Transactions on Information Theory
Near sets: toward approximation space-based object recognition
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Feature selection: near set approach
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Adaptive Rough Entropy Clustering Algorithms in Image Segmentation
Fundamenta Informaticae
Perceptually near pawlak partitions
Transactions on rough sets XII
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
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This article introduces an approach to matching 2D image segments using approximation spaces. The rough set approach introduced by Zdzisław Pawlak provides a ground for concluding to what degree a particular set of similar image segments is a part of a set of image segments representing a norm or standard. The number of features (color difference and overlap between segments) typically used to solve the image segment matching problem is small. This means that there is not enough information to permit image segment matching with high accuracy. By contrast, many more features can be used in solving the image segment matching problem using a combination of evolutionary and rough set methods. Several different uses of a Darwinian form of a genetic algorithm (GA) are introduced as a means to partition large collections of image segments into blocks of similar image segments. After filtering, the output of a GA provides a basis for finding matching segments in the context of an approximation space. A coverage form of approximation space is presented in this article. Such an approximation space makes it possible to measure the the extent that a set of image segments representing a standard covers GA-produced blocks. The contribution of this article is the introduction of an approach to matching image segments in the context of an approximation space.