Defect segmentation of fiber splicing on an industrial robot system using GMM and graph cut

A novel defect segmentation method, which utilizes both the Gaussian Mixture Model (GMM) and the Graph Cut Model (GCM), is presented to solve the defect segmentation problem of the hot image for the fiber splicing process on our industrial robot system. Since the fiber has a plastic surface, the LED lamp will create a highlight region in the fiber center when the camera collects the image data during the splicing process. Unfortunately, this highlight region always submerges the defect region. To solve this problem, both the image samples of normal mode and those of the defect mode are employed as the prior information to improve the segmentation performance. When implementing our method, first the GMM and the image samples of normal mode are used to build the statistic illumination model of the spliced fiber. The log histogram is tuned by the GMM components. Once the GMM is built, it can be utilized to restrain the highlight of the defect images. Then the GCM and the image samples of defect mode can be employed to segment the defect region and analyze their region features. Many simulation results have proved the effect of our proposed method.

[1]  Zhong Wang,et al.  Research of Double-Threshold Segmentation of Brazing-Area Defect of Saw Based on Otsu and HSV Color Space , 2009, 2009 2nd International Congress on Image and Signal Processing.

[2]  Vladimir Kolmogorov,et al.  An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Olga Veksler,et al.  Semiautomatic segmentation with compact shape prior , 2009, Image Vis. Comput..

[4]  Leopoldo Armesto,et al.  Inspection system based on artificial vision for paint defects detection on cars bodies , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Tao Zhang,et al.  Interactive graph cut based segmentation with shape priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Daijin Kim,et al.  Illumination-robust face recognition using tensor-based active appearance model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[7]  Aude Billard,et al.  Incremental learning of gestures by imitation in a humanoid robot , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[8]  Turgay Çelik,et al.  Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modeling , 2012, IEEE Transactions on Image Processing.

[9]  Atsuto Maki,et al.  Difference sphere: an approach to near light source estimation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[10]  Kaspar Althoefer,et al.  Automated pipe inspection using ANN and laser data fusion , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[11]  Kamini Kanta Mohanty,et al.  Enhancement of low light image based on Gaussian Mixture Modeling , 2010, 2010 2nd European Workshop on Visual Information Processing (EUVIP).

[12]  Olga Veksler,et al.  Semiautomatic Segmentation with Compact Shapre Prior , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[13]  Hanqing Lu,et al.  Imaging air quality evaluation based on Noise Brightness Ratio & Gompertz Type Diffusion Process , 2010, 2010 3rd International Congress on Image and Signal Processing.

[14]  Caroline Petitjean,et al.  A review of segmentation methods in short axis cardiac MR images , 2011, Medical Image Anal..

[15]  Jayong Lee,et al.  Image segmentation based on fuzzy flood fill mean shift algorihm , 2010, 2010 Annual Meeting of the North American Fuzzy Information Processing Society.

[16]  Kuo-Liang Chung,et al.  Gaussian mixture modeling of histograms for contrast enhancement , 2012, Expert Syst. Appl..

[17]  Dragoljub Surdilovic,et al.  PISA: Next Generation of Flexible Assembly Systems - From Initial Ideas to Industrial Prototypes , 2010, ISR/ROBOTIK.

[18]  Christian Riess,et al.  A common framework for ambient illumination in the dichromatic reflectance model , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[19]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Abdelhak Mahmoudi,et al.  Welding Defect Detection by Segmentation of Radiographic Images , 2009, 2009 WRI World Congress on Computer Science and Information Engineering.

[21]  Hanqing Lu,et al.  Imaging Quality based state switch algorithm for outdoor moving robot system , 2010, The 2010 IEEE International Conference on Information and Automation.

[22]  Govind P. Agrawal,et al.  Nonlinear Fiber Optics , 1989 .