A comparative study of texture coarseness measures

Authors:
J. Chamorro-Martínez;P. Martínez-Jiménez
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
Cited 1

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying high level features of texture perception

CVGIP: Graphical Models and Image Processing
Spatial Texture Analysis: A Comparative Study

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Autocovariance-based Perceptual Textural Features Corresponding to Human Visual Perception

ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Perceptually Based Metrics for the Evaluation of Textural Image Retrieval Methods

ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02

Automatic classification of archaeological pottery sherds

Journal on Computing and Cultural Heritage (JOCCH)

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are a wide variety of measures in the literature that capture the "coarseness" texture property. Some of them have better ability to represent coarseness than the others. Furthermore, some of them are more robust against the variation of other image features, like brightness, contrast, noise and size of the image. In this paper, we propose to study the robustness and the relationship with human coarseness perception of 17 classical measures of coarseness, in order to obtain a ranking of measures. This ranking can be used to identify those measures that have the highest relationship degree with perception and the least variation with the other image features.