NEW TECHNIQUE FOR IMAGE SEGMENTATION: INTRODUCTION(1)

INTRODUCTION(1)

The goals of this paper are threefold. First, want to give a general presentation of the new optimization method called BBO. Secondly, used BBO approach for image segmentation. Thirdly want to compare and contrast BBO with other population-based optimization methods. We do this by looking at the commonalities and differences from an algorithmic point-of-view, and also by comparing their performances on a set of benchmark functions. Firstly want to give some brief introduction regarding image segmentation, region growing that are need to understand before using BBO.

Image Segmentation

“Segmentation” refers to the process of dividing a digital image into multiple segments such as a sets of pixels, also known as superpixels (Chad and Hayit, 2002). The main objective of segmentation is to simplify and/or change the representation of an image into meaningful image that is more appropriate and easier to analyze. Segmentation is basically a collection of methods that allowing spatially partitioning close parts of the image as objects.

“Image segmentation” is an important aspect of digital image processing. Image segmentation may be defined as a process of assigning pixels to homogenous and disjoint regions which form a partition of the image that share certain visual characteristics (Fan, Zeng and Hacid, 2005). Image segmentation are used to locate and find objects and boundaries (lines, curves, etc.) in images. It basically aims at dividing an image into subparts based on certain feature. Features could be based on certain boundaries, contour, color, intensity or texture pattern, geometric shape or any other pattern (Pichel, Singh and Rivera,2006). It provides an easier way to analyze and represent an image. The image segmentation process consists in grouping parts of an image into units that are homogeneous with respect to one or more characteristics as shown in fig 1 (Thomas , and Dresden, 2008). The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (Bab Hadiashar and Gheissari, 2006.

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