TEXTURE FEATURE ANALYSIS OF AN IMAGE USING GRAY LEVEL CO-OCCURRENCE MATRIX.
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.
"TEXTURE FEATURE ANALYSIS OF AN IMAGE USING GRAY LEVEL CO-OCCURRENCE MATRIX.", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.7, Issue 2, page no.139-143, February-2022, Available :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: 000118813
Research Area: Computer Engineering
Country: JALGAON, Maharashtra, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
