Thresholding based on Grey Levels, Gradient Magnitude and Spatial Correlation
B.Ramesh Naik1, T.Venu Gopal2, K.Kranthi Kumar3
1Bhukya Ramesh Naik*, Department of CSE, School of Technology, GITAM deemed University, Bangalore, India.
2T. Venu Gopal., Professor, Department of CSE, JNTUH, Jagityal, Karimnagar, India.
3K. Kranthi Kumar, Department of IT, Associate Professor, SNIST, Hyderabad, India.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 26, 2020. | Manuscript published on February 10, 2020. | PP: 89-93 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1117029420/2020©BEIESP | DOI: 10.35940/ijitee.D1117.029420
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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: Image segmentation gained significant importance in recent years. The goal of segmentation is partitioning an image into distinct regions containing each pixel with similar attributes. Several Image segmentation techniques exist based on thresholding and clustering. Image segmentation based on thresholding is typically doesn’t find any objects and bounds (lines, curves, etc.) in image. To boost the segmentation performance based on thresholding strategies, a unique strategy that integrates the spacial information between pixel’s is designed. The proposed strategy utilizes pixel’s grey level Gradient magnitude and gray level spacial correlation at intervals a part to construct a unique two dimensional bar graph, known as GLGM & GLSC. This technique is valid through segmenting many real world pictures. Experimental results proved this method outperforms several existing Thresholding strategies.
Keywords: Image Segmentation, Image Thresholding, Gradient Magnitude, GLCM, GLSC.
Scope of the Article: Signal and Image Processing