Results 201 to 210 of about 36,113 (246)

Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features

International journal of imaging systems and technology (Print), 2022
Glaucoma is an eye disease in which the retinal nerve fibers are irreversibly damaged. Early identification of glaucoma is essential because it may slow the progression of the illness.
R. Patel, Manish Kashyap
semanticscholar   +1 more source

Source camera identification using GLCM

2015 IEEE International Advance Computing Conference (IACC), 2015
Digital images are becoming main focus of work for the researchers. Digital image forensics (DIF) is at the forefront of security techniques, aiming to restore the lost trust in digital imagery by uncovering digital counterfeiting techniques. Source camera identification provides different ways to identify the characteristics of the digital devices ...
Nilambari Kulkarni, Vanita Mane
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GLCM-based fingerprint recognition algorithm

2011 4th IEEE International Conference on Broadband Network and Multimedia Technology, 2011
An efficient and reliable fingerprint recognition system is the fundamental need of contemporary living. Beside forensic use, it has been deployed in a large number of commercial applications recently. In this paper, a new method for fingerprint recognition is introduced. The Core point is found initially using Poincare Index method.
Amjad Ali, Xiaojun Jing, Nasir Saleem
openaire   +1 more source

Pattern-based image retrieval using GLCM

Neural Computing and Applications, 2018
Gray-level co-occurrence matrix (GLCM) is one of the oldest techniques used for texture analysis. It has two important parameters, i.e., distance and direction. In this paper, various combinations of distance and directional angles used for GLCM calculation are analyzed in order to recognize certain patterned images based on their textural features. In
Divya Srivastava, B. Rajitha, Suneeta Agarwal, Shruti Singh   +3 more
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GLCM parameters of channel texture analysis

SEG Technical Program Expanded Abstracts 2011, 2011
Summary Channel texture is an acoustic expression of a fluvial facies derived from 3D seismic data. The Gray Level Cooccurrence Matrix (GLCM) technique has been proven to be a promising method for seismic texture analysis. However, while we try to extract seismic texture attributes, there is uncertainty on how to select the optimal GLCM parameters ...
Zhiguo Wang, Cheng Yin, Wei Zhao
openaire   +1 more source

Texture Image Segmentation Based on GLCM

Applied Mechanics and Materials, 2012
The paper proposed a method on marble texture image segmentation based on Gray Level Co-occurrence Matrix (GLCM). At first, compute the Contrast matrix on basis of GLCM. Then choose the maximum of the matrix as the threshold to segment the object. At last extract the object contour with curve fitting method.
Min Li, Jian Jun Liao
openaire   +1 more source

DCT based texture watermarking using GLCM

2010 IEEE 2nd International Advance Computing Conference (IACC), 2010
We present a novel DCT technique for digital watermarking of textured images based on the concept of gray-level co-occurrence matrix (GLCM). We provide analysis to describe the behavior of the method in terms of correlation as a function of the offset for textured images.
Sushila Kamble, Suneeta Agarwal, V.K. Shrivastava, Vikas Maheshkar   +3 more
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Oil spill detection using GLCM and MRF

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
This paper presents a study for oil spill detection in three steps. The first one considers the texture as a two dimensions array, and to describe the statistics iteration between pixels the algorithm computes a textural feature related with the Gray Level Co-occurrence Matrix (GLCM).
L Lopez, M Moctezuma, F Parmiggiani
openaire   +3 more sources

GLCM and its application in pattern recognition

2017 5th International Symposium on Computational and Business Intelligence (ISCBI), 2017
Grey Level Co-Occurrence matrix is one of the oldest techniques used for texture analysis. The Grey Level Co-Occurrence matrix has two important parameters i.e. distance and direction. In this paper various combinations of distance and directional angles used for GLCM calculation are analyzed in order to recognize certain patterned images based on ...
Shruti Singh, Divya Srivastava, Suneeta Agarwal   +2 more
openaire   +1 more source

GLCM and K-Means based Chicken Gender Classification

2021 Smart Technologies, Communication and Robotics (STCR), 2021
Machine learning plays an influential role in the agricultural sector. Intelligent machines can perform automatic management tasks in various fields. In recent years, the poultry farm management field also utilizes this concept. In poultry farms, the machine learning algorithms are employed for different purposes such as weather monitoring and control,
Thavamani S, Vijayakumar J, Sruthi K
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