By Topic

The analysis and recognition of real-world textures in three dimensions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Pei-Hsiu Suen ; Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA ; Healey, G.

The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as foreshortening, local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis textures that are required to represent the observed textures for a sample as a function of ranges of illumination and viewing angles. Basis textures are represented using multiband correlation functions that consider both within and between color band correlations. We examine properties of the dimensionality surface for real materials using the Columbia Utrecht Reflectance and Texture (CUReT) database. The analysis shows that the dependence of the dimensionality surface on ranges of illumination and viewing angles is approximately linear with a slope that depends on the complexity of the sample. We extend the analysis to consider the problem of recognizing rough surfaces in color images obtained under unknown illumination and viewing geometry. We show, using a set of 12,505 images from 61 material samples, that the information captured by the multiband correlation model allows surfaces to be recognized over a wide range of conditions. We also show that the use of color information provides significant advantages for three-dimensional texture recognition

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:22 ,  Issue: 5 )