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Image Segmentation, a basic technique for many real world applications, has been considered in this paper. The Seeded Region Growing (SRG) algorithm, as the first and probably the simplest region growing algorithm, faces three important problems: the position of seeds, the number of seeds, and region growing strategy. Two new versions of SRG are introduced(More)
Introducing methods that can work out the problem of noisy image segmentation is necessary for real-world vision problems. This paper proposes a new computational algorithm for segmentation of gray images contaminated with impulse noise. We have used Fuzzy C-Means (FCM) in fusion with Particle Swarm Optimization (PSO) to define a new similarity metric based(More)
Color image segmentation, a problem with more than one solution, could be faced as a process of categorizing a color image into several homogen regions containing similar objects. In this paper a new and effective unsupervised color image segmentation method is introduced which utilizes three main kinds of features. These features fall in the domain of(More)
The necessity of proposing algorithms that are effective in noisy image segmentation is clear in many real-world applications. This paper proposes a new algorithm for severely noisy image segmentation by looking at the proper choice of feature, and feature manipulation. We are using Discrete Wavelet Transformation (DWT) as a tool to provide our method with(More)
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