Image segmentation is usually accustomed distinguish the foreground from the background of an image. The main target of this paper is an effort to review Image Segmentation using Thresholding Technique on a picture corrupted by Gaussian Noise as well as Salt and Pepper Noise which is enforced using MATLAB software and the results obtained are studied and thereby mentioned, highlighting the techniques performance. The algorithm is demonstrated through the segmentation of color images. The classification accuracy of the proposed method is evaluated and a comparative study versus existing techniques is presented. The experiments were conducted on an extensive set of color images. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.

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A novel approach to simulate Cellular Neural Networks (CNN) is presented in this paper. The approach, time-multiplexing simulation, is prompted by the need to simulate hardware models and test hardware implementations of CNN. For practical applications, due to hardware limitations, it is impossible to have a one-to-one mapping between the CNN hardware processors and all the pixels of the image. This simulator provides a solution by processing the input image block by block, with the number of pixels in a block being the same as the number of CNN processors in the hardware. The algorithm for implementing this simulator is presented along with popular numerical integration algorithms. Some simulation results and comparisons are also presented.