Application of Genetic Algorithm for Image Enhancement and Segmentation.  

Abstract

Genetic algorithm is the type of Soft Computing method. The Genetic Algorithm (GA) is a model of machine learning which derives its behavior from a metaphor of the processes of evolution in nature. The aim is to enhance the quality of the image and to convert the image into segments to get more meaningful image and it will be easy to analyze the image using genetic algorithm. Genetic algorithm is the unbiased optimization technique. It is useful in image enhancement and segmentation. GA was proven to be the most powerful optimization technique in a large solution space. This explains the increasing popularity of GAs applications in image processing and other fields. Genetic Algorithms (GAs) are increasingly being explored in many areas of image analysis to solve complex optimization problems. This paper gives a brief overview of the canonical genetic algorithm and it also reviews the tasks of image pre-processing. The main task of machine vision is to enhance image quality with respect to get a required image per-ception. The GAs were adopted to achieve better results, faster processing times and more specialized applications. This paper introduces various approaches based on genetic algorithm to get image with good and natural contrast. The image enhancement is the most fundamental image processing tasks. And Image Segmentation is very difficult task. This paper includes the definition of image enhancement and image segmentation and also the need of Image Enhancement and the image can be enhanced using the Genetic Algorithm and the Image Segmentation using Genetic Algorithm.  

Authors and Affiliations

Miss. Komal R. Hole , Prof. Vijay S. Gulhane , Prof. Nitin D. Shellokar

Keywords

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  • EP ID EP162008
  • DOI -
  • Views 79
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How To Cite

Miss. Komal R. Hole, Prof. Vijay S. Gulhane, Prof. Nitin D. Shellokar (2013). Application of Genetic Algorithm for Image Enhancement and Segmentation.  . International Journal of Advanced Research in Computer Engineering & Technology(IJARCET), 2(4), 1342-1346. https://europub.co.uk/articles/-A-162008