Split and Merge Based Quantitative Approach to Select Filter Combination for Image Segmentation

Abstract

Abstract. With the advent of image analysis and computation in different domains, image segmentation has emerged as the most crucial step to achieve a compact segment-based description of image scene by decomposing it into meaningful segments of similar attributes. The pre-and-post filtering operation reduces the effect of noise from the segmented image. The Cameraman image is pre-filtered using Laplacian, Median and Min filter. The Split and Merge method for Region based image segmentation which guarantees to connected regions are now applied on the filtered image. The Median, Laplacian and Sobel filter is then used to post-filter the segmented image. The PSNR and MSE values are calculated to quantitative evaluation of segmented images. The quantitative evaluation of post-filtered segmented image shows that median filter produces most effective result with lowest MSE of 84.89 dB and highest PSNR of 5.72 dB.

Performance Analysis of Edges, Corners and the genres: A Subjective Estimation

“…The major advantage of digital image processing is the availability of means of avoiding noise or distortion in processing of generated signals [1]. Digital Image Processing comprises of various steps starting from acquiring an image within the problem domain to identification of desired object [23]. Before approaching to object recognition, the image is compressed and partitioned into constituent objects to derive the descriptor of the partitioned segment.…”