Edge Detection of Different Images using Soft Computing Techniques
Naveen Singh Dagar1, Pawan Kumar Dahiya2
1Naveen Singh Dagar, DeenbandhuChhotu Ram University of Science & Technology, Murthal, Haryana, India
2Pawan Kumar Dahiya, DeenbandhuChhotu Ram University of Science & Technology, Murthal, Haryana, India
Manuscript received on 01 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 222-226 | Volume-8 Issue-2, July 2019 | Retrieval Number: A2976058119/19©BEIESP | DOI: 10.35940/ijrte.A2976.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The technique by which an image or photograph is divided into several number of parts in order to analyze the segmented components such as colors, textures grey scale and edges/boundaries of the entities which are present in the image is called as image segmentations. Images obtained by segmentation methods are more understandable as compared to the original images. In the digital snap shot segmentation is essentially used to detect object boundaries present in the image. The paper presents the comparative analysis of image segmentation using soft computing methods.In this paper, we included genetic algorithm, ant colony algorithm, neural network, neuro-fuzzy genetic and adaptive neuro-fuzzy inference system. The techniques are tested on six standard test images. The peak signal to noise ratio (PSNR)is calculated for GA and ACO techniques. The results which are obtained by the above techniques prove that the value of PSNR for GA is much more as compared to the ACO technique.
Keywords: Segmentation, Soft Computing, ACO, GA, ANFIS, ANN, PSNR.
Scope of the Article: Soft Computing