A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
A segmentation algorithm for noisy images: design and evalution
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A hierarchical evolutionary algorithm for automatic medical image segmentation
Expert Systems with Applications: An International Journal
Image segmentation based on genetic algorithms combination
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
An adaptive clustering algorithm for image segmentation
IEEE Transactions on Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
One-pixel-wide closed boundary identification
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A genetic algorithm for color image segmentation
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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A graph-based approach for image segmentation that employs genetic algorithms is proposed. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A fitness function, that extends the normalized cut criterion, is employed, and a new concept of nearest neighbor, that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is defined. Because of the locus-based representation of individuals, the method is able to partition images without the need to set the number of segments beforehand. As experimental results show, our approach is able to segment images in a number of regions that well adhere to the human visual perception.