Contour Extraction and Quality Inspection for Inner Structure of Deep Hole Components

This paper focuses on the contour extraction for the inner wires of a kind of deep hole component to achieve a high-accuracy inspection. The vision system consisting of a camera and an endoscope is developed to acquire high-quality images of the internal structure. For the acquired images, a contour extraction method is proposed, which could be divided into the following steps. First, the start points of wires referred as the prior information on the component are obtained with a predefined filter, and on the basis, several regions of interest (ROIs) are defined. Second, the multiscale probability of boundary operator is utilized to detect edges in the ROIs. Third, a Brownian motion model is established to calculate the connectivity between edges. The prior information obtained previously is used again to determine the probabilities of the edges belonging to the contours. Finally, the symmetric ratio contour method is used to form the wires’ contours with the edges. In the proposed method, the edges belonging to the wires’ contours are enhanced by making full use of the prior information, resulting in the improvement in accuracy and real-time performance. As evidenced by the experiments, the proposed method can efficiently extract the inner wires’ contours from the component’s image with low-contrast conditions, noises, and shadows.

[1]  Xiaofeng Ren,et al.  Multi-scale Improves Boundary Detection in Natural Images , 2008, ECCV.

[2]  Michael Lindenbaum,et al.  A Generic Grouping Algorithm and Its Quantitative Analysis , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jun Wang,et al.  Salient closed boundary extraction with ratio contour , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Lance R. Williams,et al.  Segmentation of Multiple Salient Closed Contours from Real Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[6]  Han Wang,et al.  A New IC Solder Joint Inspection Method for an Automatic Optical Inspection System Based on an Improved Visual Background Extraction Algorithm , 2016, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[7]  Lance R. Williams,et al.  Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience , 1995, Neural Computation.

[8]  Song Wang,et al.  Globally Optimal Grouping for Symmetric Closed Boundaries by Combining Boundary and Region Information , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  K. Thornber,et al.  Analytic solution of stochastic completion fields , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[10]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Jitendra Malik,et al.  Using contours to detect and localize junctions in natural images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Yuan Yan Tang,et al.  A Hybrid of Local and Global Saliencies for Detecting Image Salient Region and Appearance , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Han Wang,et al.  IC Solder Joint Inspection Based on an Adaptive-Template Method , 2018, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[14]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Joachim M. Buhmann,et al.  Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Xiaofeng Ren,et al.  Discriminatively Trained Sparse Code Gradients for Contour Detection , 2012, NIPS.

[17]  Ian H. Jermyn,et al.  Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Nicolai Petkov,et al.  Edge and line oriented contour detection: State of the art , 2011, Image Vis. Comput..

[20]  De Xu,et al.  Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks , 2018, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[21]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Jonathon A. Chambers,et al.  Robust Iris Segmentation Method Based on a New Active Contour Force With a Noncircular Normalization , 2017, IEEE Trans. Syst. Man Cybern. Syst..

[23]  Hongdong Li,et al.  Winding Number Constrained Contour Detection , 2015, IEEE Transactions on Image Processing.

[24]  Lattre de Tassigny Boundary Extraction in Natural Images Using Ultrametric Contour Maps , 2006 .