Open AccessThresholding based on Grey Levels, Gradient Magnitude and Spatial Correlation
Author(s) -
B. Ramesh Naik,
T. Venu Gopal,
K. Kranthi Kumar
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.d1117.029420
Subject(s) - thresholding , artificial intelligence , pixel , segmentation , image segmentation , pattern recognition (psychology) , computer science , computer vision , region growing , balanced histogram thresholding , cluster analysis , image (mathematics) , grayscale , scale space segmentation , mathematics , image processing , histogram equalization
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.