Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30131
Title: Entropy-based fabric weave pattern indexing and classification
Authors: Zheng, D
Baciu, G 
Hu, J 
Keywords: Classification
Content-based
Entropy
FFT
Index
Weave pattern
Issue Date: 2010
Source: International journal of cognitive informatics and natural intelligence, 2010, v. 4, no. 4, p. 76-92 How to cite?
Journal: International Journal of Cognitive Informatics and Natural Intelligence 
Abstract: In textile design, fabric weave pattern indexing and searching require extensive manual operations. There has been little or no research on index and efficient search algorithms for fabric weave patterns. In this regard, we propose a method to index and search fabric weave patterns. The paper uses pattern clusters, boundary description code, neighbor transitions, Entropy and Fast Fourier Transform (FFT) directionality as a hybrid approach for the cognitive analysis of fabric texture. Then, we perform a comparison and classification of a wide variety of weave patterns. There are three common patterns used in textile design: (1) plain weave, (2) twill weave, and (3) satin weave. First, we classify weave patterns into these three categories according to the industrial weave pattern definition and weave point distribution characteristics. Second, we use FFT to describe the weave point distribution. Finally, an Entropy-based method is used to compute the weave point distribution and use this to generate a significant texture index value. Our experiments show that the proposedapproach achieves the expected match for classifying and prioratizing weave texture patterns.
URI: http://hdl.handle.net/10397/30131
ISSN: 1557-3958
DOI: 10.4018/jcini.2010100106
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