Fast processing of foreign fiber images by image blocking

In the textile industry, it is always the case that cotton products are constitutive of many types of foreign fibers which affect the overall quality of cotton products. As the foundation of the foreign fiber automated inspection, image process exerts a critical impact on the process of foreign fiber identification. This paper presents a new approach for the fast processing of foreign fiber images. This approach includes five main steps, image block, image pre-decision, image background extraction, image enhancement and segmentation, and image connection. At first, the captured color images were transformed into gray-scale images; followed by the inversion of gray-scale of the transformed images ; then the whole image was divided into several blocks. Thereafter, the subsequent step is to judge which image block contains the target foreign fiber image through image pre-decision. Then we segment the image block via OSTU which possibly contains target images after background eradication and image strengthening. Finally, we connect those relevant segmented image blocks to get an intact and clear foreign fiber target image. The experimental result shows that this method of segmentation has the advantage of accuracy and speed over the other segmentation methods. On the other hand, this method also connects the target image that produce fractures therefore getting an intact and clear foreign fiber target image.

[1]  Daoliang Li,et al.  A new approach for image processing in foreign fiber detection , 2009 .

[2]  Dong-Jo Park,et al.  Fast image segmentation based on multi-resolution analysis and wavelets , 2003, Pattern Recognit. Lett..

[3]  Thomas Batard,et al.  Clifford Algebra Bundles to Multidimensional Image Segmentation , 2010 .

[4]  Weihua Gui,et al.  Clustering-driven watershed adaptive segmentation of bubble image , 2010 .

[5]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[6]  Hsi-Jian Lee,et al.  Document image binarization by two-stage block extraction and background intensity determination , 2007, Pattern Analysis and Applications.

[7]  Ta-Te Lin,et al.  An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits , 2008 .

[8]  J. Michael Fried,et al.  Multichannel image segmentation using adaptive finite elements , 2009 .

[9]  J. Furst,et al.  A Hybrid Approach for Liver Segmentation , 2007 .

[10]  Francisco F. Rivera,et al.  Image segmentation based on merging of sub-optimal segmentations , 2006, Pattern Recognit. Lett..

[11]  Akinobu Shimizu,et al.  Automatic Liver Segmentation Method based on Maximum A Posterior Probability Estimation and Level Set Method , 2007 .

[12]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

[13]  Zhi-Hua Zhou,et al.  SOM Ensemble-Based Image Segmentation , 2004, Neural Processing Letters.

[14]  Gui Yun Tian,et al.  A machine vision system for on-line removal of contaminants in wool , 2006 .

[15]  Frank W. Bentrem.  A Q-Ising model application for linear-time image segmentation , 2010 .

[16]  Daoliang Li,et al.  A fast segmentation method for high-resolution color images of foreign fibers in cotton , 2011 .

[17]  Thomas Lange,et al.  Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model , 2007 .

[18]  Adel M. Alimi,et al.  Complex documents images segmentation based on steerable pyramid features , 2010, International Journal on Document Analysis and Recognition (IJDAR).

[19]  Daoliang Li,et al.  Original paper: Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine , 2010 .

[20]  Paul Suetens,et al.  Landmark based liver segmentation using local shape and local intensity models , 2007 .

[21]  Georgios Tziritas,et al.  Colour and texture segmentation using wavelet frame analysis, deterministic relaxation, and fast marching algorithms , 2004, J. Vis. Commun. Image Represent..

[22]  Maria Athelogou,et al.  Cognition Network Technology for a Fully Automated 3D Segmentation of Liver , 2007 .

[23]  G. van Straten,et al.  A vision based row detection system for sugar beet , 2005 .

[24]  Mateu Sbert,et al.  Image Segmentation Using Excess Entropy , 2009, J. Signal Process. Syst..