A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Optimal thresholding—a new approach
Pattern Recognition Letters
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Image Segmentation Approach Based on Particle Swarm Optimization
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Improvement of Grayscale Image Segmentation Based on PSO Algorithm
ICCIT '09 Proceedings of the 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology
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Image segmentation is one of the fundamental and important steps that is needed to prepare an image for further processing in many computer vision applications. Over the last few decades, many image segmentation methods have been proposed as accurate image segmentation is vitally important for many image, video and computer vision applications. A common approach is to look at the grey level intensities of the image to perform multi-level-thresholding. In our approach we treat image segmentation as an optimization problem to identify the most appropriate segments for a given image where a two-stage population based stochastic optimization with a final refinement stage has been adopted. Nevertheless, the ability to quantify and compare the resulting segmented images can be a major challenge. Information theoretic measures will be used to provide a quantifiable measure of the segmented images. These measures would also be compared with the total distances of the pixels to its centroid for each region.