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International Journal of Computational Engineering Research||Vol, 03||Issue, 5|| Image Segmentation Using RGB Decomposition and Modified Bacterial Foraging Optimization Bobbinpreet Kaur 1, Raju Sharma 2 1 Student BBSBEC Fatehgarh Sahib, 2 Assistant Professor, BBSBEC Fatehgarh Sahib ABSTRACT: This paper addresses the problem of segmenting the image into various color components by using combined approach of Modified Bacterial foraging optimization approach and RGB Decomposition. The original image is decomposed into separate planes of R G and B and then modified Bacterial Foraging algorithm is applied on three planes separately to calculate three different thresholds. Segmentation is performed on the basis of Thresholding. Since Image segmentation is the basic step in many image processing applications so faithful segmentation algorithm must be developed for successful implementation of the processing applications. Main aim of image segmentation is to extract the information which is of interest for a particular application. This methodology will be able to separate three different colors of original image. The accuracy of any algorithm varies with input image. This is an extensive research area as many applications depend upon results of the algorithm for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate results than another for all type of images. KEYWORDS: Modified BFO, RGB planes, Segmentation, Thresholding I. INTRODUCTION Image segmentation deals with dividing the image according to either similarity or dissimilarity. It is an important prospect in image analysis. Color is a perceptual phenomenon related to human response to different wavelengths in the visible electromagnetic spectrum [1].Color is the most prominent feature of any image. Extracting color information from any image has many applications related to computer vision algorithms. Color of an image can carry much more information than gray level [2]. In a broad sense the colored images segmentation are classified as follows:- IMAGE SEGMENTATION SIMILARITY BASED (Basis for segmentation is similarity according to some attribute e.g. Intensity, color etc) DISCONTINUITY BASED (Basis for segmentation is abrupt transition in intensity level) Figure 1:- Types of Image Segmentation 1.1 Similarity Based Segmentation Techniques:- Similarity based segmentation techniques consists of Thresholding methods and region based methods. Thresholding methods convert grayscale image into binary image (Black and white) image by first choosing a www.<strong>ijcer</strong>online.com ||May ||2013|| Page 50
Image Segmentation using RGB… gray level in the original image and then turning every pixel black or white according to whether its gray value is greater or less than T.[3] A pixel = White if gray value >T = Black if gray value
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