This paper focuses on marker based watershed segmentation algorithms. As marker based watershed segmentation algorithm causes over segmentation and cause noise in the image produced. So to reduce these problem different researchers has proposed different solutions, but the best solution is to use bilateral filter. The main objective of this paper is to find the gaps in existing literature. The different segmentation techniques are reviewed and found that marker based is best in most of cases because it marks the regions then segment them. But optimizing the marking regions is still an area of research.
Abstract:The Singular Value Decomposition expresses image data in terms of number of Eigen vectors depending upon the dimension of an image. The psycho visual redundancies in an image are used for compression. Thus an image can be compressed without affecting the image quality. This paper presents one such image compression technique called as SVD. Basic mathematics of SVD is dealt with in detail and results of applying SVD on an image are also discussed. The MSE and compression ratio are used as thresholding, parameters for reconstruction. SVD is applied on variety of images for experimentation. The work is concentrated to reduce the number of eigen values required to reconstruct an image.
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