TEXTURE FEATURE ANALYSIS OF AN IMAGE USING GRAY LEVEL CO-OCCURRENCE MATRIX.

Registration ID: IJNRD_180498

Published ID: IJNRD2202021

DOI: Click Here to Get

Khushal U. Sarode , Rajat Savdekar , Tushar Chaudhari

GLCM, feature extraction, gui, image processing, texture values,Contrast group, Orderliness group, Statistics group, Graphs.

In this project we are analyzing features of image using the GLCM approach. There are various features calculated from Gray Level Co-Occurrence Matrix (GLCM) which helps us to understand the overall image. Our main goal in the project is to implement a calculating GLCM matrix using texture feature extraction of an image. GLCM is widely used for evaluating texture features that is used for classification of images. There are many features listed by author Haralick but in this we have considered the most commonly used features which includes energy , entropy, homogeneity, dissimilarity, angular second moment, glcm mean, glcm correlation, glcm variance, max probability and contrast Texture features are divided into two parts: Image processing and texture feature analysis. So basically at the end will be taking an image as an input and output will also be an image with a feature.

Published Paper   E-Certificate

Khushal U. Sarode, Rajat Savdekar, & Tushar Chaudhari (February-2022). TEXTURE FEATURE ANALYSIS OF AN IMAGE USING GRAY LEVEL CO-OCCURRENCE MATRIX.. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(2), 139-143. https://ijnrd.org/papers/IJNRD2202021.pdf

Volume 7 Issue 2, February-2022

Pages : 139-143

Paper Reg. ID: IJNRD_180498

Published Paper Id: IJNRD2202021

Downloads: 000121989

Research Area: Computer Engineering 

Country: JALGAON, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2202021.pdf

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