@inproceedings{692957bfa81442ce835143254f00882f,
title = "Texture segmentation using different orientations of GLCM features",
abstract = "This paper describes the development of a new texture based segmentation algorithm which uses a set of features extracted from Grey-Level Co-occurrence Matrices. The proposed method segments different textures based on noise reduced features which are effective texture descriptor. Each of the features is processed including normalisation and noise removal. Principal Component Analysis is used to reduce the dimensionality of the resulting feature space. Gaussian Mixture Modelling is used for the subsequent segmentation and false positive regions are removed using morphology. The evaluation includes a wide range of textures (more than 80 Brodatz textures) and in comparison (both qualitative and quantitative) with state of the art techniques very good segmentation results have been obtained. ",
keywords = "Gaussian mixture modeling, computer vision, data normalisation/smoothing, grey level co-occurrence matrix, texture segmentation",
author = "Rampun, \{Yambu Andrik\} and Harry Strange and Reyer Zwiggelaar",
year = "2013",
month = jun,
day = "6",
doi = "10.1145/2466715.2466720",
language = "English",
isbn = "978-1-4503-2023-8",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "1--8",
booktitle = "MIRAGE 2013 - Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications",
address = "United States of America",
}