Fiber Recognition of PET/Rayon Composite Yarn Cross-sections Using Voting Techniques

In this paper, we propose a new image processing method to recognize the fiber patterns in the image of PET/Rayon composite yarn cross-section. Our method consists of two voting techniques: the connected component voting (for obtaining single fiber locations) and the circle parameter voting (circle detection, for recognizing the fiber patterns). When compared with the previous approach, the new method needs fewer parameters and is more flexible. Several real images are used to show the performance of the proposed method.

[1]  Andrew F. Laine,et al.  Circle recognition through a 2D Hough Transform and radius histogramming , 1999, Image Vis. Comput..

[2]  Hazem M. Abbas,et al.  A statistical approach for textile fault detection , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[3]  Bugao Xu,et al.  Fiber-image Analysis Part I: Fiber-image Enhancement , 1996 .

[4]  Devron Thibodeaux,et al.  Cotton Fiber Maturity by Image Analysis , 1986 .

[5]  O DudaRichard,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972 .

[6]  Naoki Saito,et al.  A Method to Detect and Characterize Ellipses Using the Hough Transform , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Yonina C. Eldar,et al.  A probabilistic Hough transform , 1991, Pattern Recognit..

[8]  Hungwen Li,et al.  Fast Hough transform: A hierarchical approach , 1986, Comput. Vis. Graph. Image Process..

[9]  Shih-Hsuan Chiu,et al.  Fiber Recognition and Distribution Analysis of PET/Rayon Composite Yarn Cross Sections Using Image Processing Techniques , 1999 .

[10]  Shih-hsuan Chiu,et al.  Textural Defect Segmentation Using a Fourier-Domain Maximum Likelihood Estimation Method , 2002 .

[11]  Bugao Xu,et al.  Characterizing Fiber Crimp by Image Analysis: Definitions, Algorithms, and Techniques , 1992 .

[12]  Peyman Milanfar.  On the hough transform of a polygon , 1996, Pattern Recognit. Lett..

[13]  Josef Kittler,et al.  The Adaptive Hough Transform , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[15]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[16]  Shih-hsuan Chiu,et al.  Defect Segmentation of Texture Images with Wavelet Transform and a Co-occurrence Matrix , 2001 .

[17]  Opas Chutatape,et al.  A modified Hough transform for line detection and its performance , 1999, Pattern Recognit..

[18]  Che-Yen Wen,et al.  The safety helmet detection for ATM's surveillance system via the modified Hough transform , 2003, IEEE 37th Annual 2003 International Carnahan Conference onSecurity Technology, 2003. Proceedings..

[19]  Jaume Escofet,et al.  Automatic quality control of textile webs by image processing , 1999, Iberoamerican Meeting of Optics and the Latin American Meeting of Optics, Lasers and Their Applications.

[20]  BennettNick,et al.  A Method to Detect and Characterize Ellipses Using the Hough Transform , 1999 .

[21]  Helge J. Ritter,et al.  Artificial neural networks for automated quality control of textile seams , 1999, Pattern Recognit..

[22]  Mohammed Atiquzzaman,et al.  Multiresolution Hough Transform-An Efficient Method of Detecting Patterns in Images , 1992, IEEE Trans. Pattern Anal. Mach. Intell..