Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Comparison of Edge Detectors: A Methodology and Initial Study
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Constraint-based Fuzzy Optimization Data Fusion for Sensor Network Localization
SKG '06 Proceedings of the Second International Conference on Semantics, Knowledge, and Grid
A vision chip for color segmentation and pattern matching
EURASIP Journal on Applied Signal Processing
Colour vision model-based approach for segmentation of traffic signs
Journal on Image and Video Processing - Color in Image and Video Processing
Generalization of the Dempster-Shafer theory: a fuzzy-valued measure
IEEE Transactions on Fuzzy Systems
Fuzzy homogeneity approach to multilevel thresholding
IEEE Transactions on Image Processing
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Adaptive color segmentation-a comparison of neural and statistical methods
IEEE Transactions on Neural Networks
Landmark detection for autonomous spacecraft landing on mars
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
A novel pyramidal dual-tree directional filter bank domain color image watermarking algorithm
ICICS'11 Proceedings of the 13th international conference on Information and communications security
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
Object extraction from T2 weighted brain MR image using histogram based gradient calculation
Pattern Recognition Letters
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A novel method of colour image segmentation based on fuzzy homogeneity and data fusion techniques is presented. The general idea of mass function estimation in the Dempster-Shafer evidence theory of the histogram is extended to the homogeneity domain. The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and the classification accuracy for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results demonstrate the superiority of introducing the fuzzy homogeneity method in evidence theory for image segmentation.