Intelligent texture-based pattern search, classification and interpolation for woven fabric design

Pao Yue-kong Library Electronic Theses Database

Intelligent texture-based pattern search, classification and interpolation for woven fabric design

 

Author: Zheng, Dejun
Title: Intelligent texture-based pattern search, classification and interpolation for woven fabric design
Degree: Ph.D.
Year: 2012
Subject: Textile design -- Data processing.
Textile fabrics -- Design.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: 210 p. : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2551304
URI: http://theses.lib.polyu.edu.hk/handle/200/6814
Abstract: In the cognitive process of design activity, fabric designers conceive the color and texture composition not individually, but as an ensemble of tones, shades and tints that are created in the texture patterns of yarn and fiber materials. Perceptual features of natural textures as well as fabric textures have been extensively studied in the existing literature. However, no thorough investigation of the cognitive texture features of woven patterns in fabric design has been conducted so far. The present research uses cognitive informatics models to study fabric texture features in the process of woven fabric design. It provides a comprehensive framework to facilitate selecting and designing the fabric textures in the design process. The research framework comprises cognitive fabric feature analysis and fabric texture operations in fabric pattern design, namely, fabric search, pattern classification, and woven texture interpolation with color theme-based texture synthesis. A novel object-attribute-relation (OAR) model is used to study fabric texture digitization and texture feature analysis. A relation between the high-level cognitive features and low-level perceptual features of fabric patterns in design activity is described. The cognitive features in fabric design are used to develop fabric texture operations. Examples of how cognitive features can be used to perform texture selecting and synthesizing tasks are given. There are three major contributions of this study to existing fabric texture analysis and research. (1) The study reduces the gap between the cognitive features of fabric textures in the design activity and the perceptual features of the textures in material operations. (2) New approaches for fabric pattern design are developed based on the cognitive color theme and interpolated woven patterns. (3) The research findings illustrate that fabric texture digitization methods and cognitive feature extraction in design activity are major factors in developing effective fabric texture operations.

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