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Image Segmentation Optimization

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Image Segmentation Optimization using Ant Weight Lifting Algorithm H C Abhishek 1, G Tirumala Rao2 1M.Tech Student, 2 Professor 12Gayatri Vidya Parishad College of Engineering(A) Visakhapatnam, Pin no.530048 1abhishek.14311@yahoo.com, 2tirumalagrao@gvpce.ac.in Abstract— Image segmentation is an excellent concept in the field of image processing and is one of most popular platform of research. To properly segment an image in to well-defined classes watershed segmentation, k-means clustering and quad tree segmentation have been researched. Segmentation techniques can be broadly classified such as thresholding techniques, clustering techniques, region based and merging, edge detection techniques. Optimization in Image segmentation results in …show more content…

At first, a threshold value is selected based on application. Pixels with strength greater than the threshold are assigned a value of 1 and all other pixels assigned 0.This process divides an image into two zones and thus creates a binary image. Basically, it divides the darker and the lighter regions. Many parameters such as intensity characters and size of objects are taken into consideration while selecting the threshold value. Several types of thresholding like multilevel, hysteresis and optimal but the concept remains same. Disadvantages in selecting thresholding becomes quite difficult to determine the optimum threshold value and also does not give satisfactory results in case of a large number of objects, its primary usage being in cases where we need to separate objects from the …show more content…

The objects which are in closed boundaries are then filled and thus segmented image is formed. The detection of closed boundaries is a main problem caused due to noise hampering the image quality. [5] Watershed segmentation mainly performs the segmentation by flooding the areas that are determined as watersheds by using a local minima function and then linking them together by creating watersheds. It comes along with a major drawback in form of over-segmentation which requires a lot of pre and post-processing to be implemented on the image to give optimum results. Another segmentation technique [6] is the Quadtree segmentation which implements an image to be subdivided into four equal squares if it does not meet a pre-selected homogeneity criterion. Edges usually do not meet the requirement of a homogeneity criterion and thus quadtree segmentation often results in a lot of segmented squares at the edges giving improper

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