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Graphical processing unit–based machine vision system for simultaneous measurement of shrinkage and soil release in fabrics

[+] Author Affiliations
Sridharan Kamalakannan

Texas Tech University, Department of Electrical and Computer Engineering, Applied Vision Laboratory, 2500 Broadway, Lubbock, Texas 79409

Arunkumar Gururajan

Texas Tech University, Department of Electrical and Computer Engineering, Applied Vision Laboratory, 2500 Broadway, Lubbock, Texas 79409

Matthew Hill

Texas Tech University, Department of Electrical and Computer Engineering, Applied Vision Laboratory, 2500 Broadway, Lubbock, Texas 79409

Muneem Shahriar

Texas Tech University, Department of Electrical and Computer Engineering, Applied Vision Laboratory, 2500 Broadway, Lubbock, Texas 79409

Hamed Sari-Sarraf

Texas Tech University, Department of Electrical and Computer Engineering, Applied Vision Laboratory, 2500 Broadway, Lubbock, Texas 79409

Eric F. Hequet

Texas Tech University, Fiber and Biopolymer Research Institute, 1001 East Loop 289, Lubbock, Texas 79403

J. Electron. Imaging. 19(2), 023009 (May 05, 2010). doi:10.1117/1.3421977
History: Received December 20, 2009; Revised March 17, 2010; Accepted March 23, 2010; Published May 05, 2010; Online May 05, 2010
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We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach.

© 2010 SPIE and IS&T

Citation

Sridharan Kamalakannan ; Arunkumar Gururajan ; Matthew Hill ; Muneem Shahriar ; Hamed Sari-Sarraf, et al.
"Graphical processing unit–based machine vision system for simultaneous measurement of shrinkage and soil release in fabrics", J. Electron. Imaging. 19(2), 023009 (May 05, 2010). ; http://dx.doi.org/10.1117/1.3421977


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