One of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in
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One of the sciences in order to increase the efficiency of intelligent systems to be used in the visual sense, is Machine vision science. The first step in many applications in machine vision is image segmentation. Image segmentation, refers to the grouping of pixels in an image So that these pixels, the same qualities have with each other And the pixels adjacent parts, have different characteristics. The most important feature used in image segmentation, colors and features. In monochrome images, the gray level is considered as properties But color images, different color spaces used as a color feature. In this study, the color and texture features for image segmentation is considered. Clustering-based methods of are used in image segmentation methods and Gaussian function is similar measure in clustering images. Spectral clustering requires has high computational cost. To save time and accelerate the segmentation of images Using clustering with Super pixels will achieve optimal results And to achieve reliable results approximate and fuzzy algorithm is used. The proposed algorithm is applied on several standard image And the evaluation criteria,Evaluated and evaluated by the indicators are evaluated and compared. The results of the experiments were compared to other fragmentation methods, suggesting a 3.4% superiority in the segmentation accuracy of the proposed algorithm, and all the evaluation indicators of the study have increased to a satisfactory level.
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