A closer look at texture metrics for visualization

Haleh Hagh-Shenas; Victoria Interrante; Cheong Hee-Park

doi:10.1117/12.643150

9 February 2006 A closer look at texture metrics for visualization

Haleh Hagh-Shenas, Victoria Interrante, Cheong Hee-Park

Proceedings Volume 6057, Human Vision and Electronic Imaging XI; 60570W (2006) https://doi.org/10.1117/12.643150
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

This paper presents some insights into perceptual metrics for texture pattern categorization. An increasing number of researchers in the field of visualization are trying to exploit texture patterns to overcome the innate limitations of three dimensional color spaces. However, a comprehensive understanding of the most important features by which people group textures is essential for effective texture utilization in visualization. There have been a number of studies aiming at finding the perceptual dimensions of the texture. However, in order to use texture for multivariate visualization we need to first realize the circumstances under which each of these classification holds. In this paper we discuss the results of our three recent studies intended to gain greater insight into perceptual texture metrics. The first and second experiments investigate the role that orientation, scale and contrast play in characterizing a texture pattern. The third experiment is designed to understand the perceptual rules people utilize in arranging texture patterns based on the perceived directionality. Finally, in our last section we present our current effort in designing a computational method which orders the input textures based on directionality and explain its correlation with the human study.

Citation Download Citation

Haleh Hagh-Shenas, Victoria Interrante, and Cheong Hee-Park "A closer look at texture metrics for visualization", Proc. SPIE 6057, Human Vision and Electronic Imaging XI, 60570W (9 February 2006); https://doi.org/10.1117/12.643150

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KEYWORDS

Visualization

Image classification

Visual process modeling

Statistical analysis

Computer graphics

Electronic imaging

Fourier transforms

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