By Topic

Identification for animal fibers with artificial neural network

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)
Shi, Xian-Jun ; Coll. of Sci., Wuhan Univ. of Sci. & Eng., Wuhan ; Yu, Wei-Dong

Scale pattern of animal fibers is different and that is a major reference distinguishing them from each other. Usually, there are four basic shape parameters, including fiber diameters, scale interval, scale perimeter and scale area, to be used for describing the cuticle scale pattern of animal fiber. In present paper, two kinds of animal fiber are checked up under light microscope with a magnification of 40timesfor objective and their images are captured by a CCD camera fixed on the microscope. After using a series of image operators on them, the skeletonized binary images only having one pixel wide can be obtained. Then, these basic shape parameters of scale are measured and the database composed of numerical data of four comparable indexes, including fiber diameter, scale interval, normalized scale perimeter and normalized scale area, are established. Finally, a multi-parameter neural network classifier, including four input nodes, five hidden nodes and two output nodes, are developed to classify the two kinds of animal fibers. Two sets of classification rules are applied to the classifier respectively and the simulation results show that whether rule 1 or 2, the neural network classifier can always distinguish cashmere from fine wool (70s) effectively and the average classification performance is higher than 90 percent.

Published in:

Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. International Conference on  (Volume:1 )

Date of Conference:

30-31 Aug. 2008