A machine vision system with an irregular imaging function

Traditional machine vision systems sample images at a fixed resolution and a presetting speed in one inspection. When detecting small defects in a large background, there is usually a large amount of redundancy in the image data. To solve this problem, a machine vision system with an irregular imaging function is presented in the paper. The system can irregularly sample images with a high resolution in the defect area and a low resolution in the background area, which reduces the total data in image processing and increases the speed and accuracy of real-time inspection significantly.

[1]  S. Thorpe,et al.  Speed of processing in the human visual system , 1996, Nature.

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

[3]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

[4]  Peter Kovesi,et al.  Phase Congruency Detects Corners and Edges , 2003, DICTA.

[5]  R. C. Grimsdale Automatic Interpretation and Classification of Images, A. Grasselli (Ed.). Academic Press, New York (1969), 436 pp. $14.00. , 1972 .

[6]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[7]  Arun Ross,et al.  Fingerprint mosaicking , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Michelle R. Greene,et al.  Natural Scene Categorization from Conjunctions of Ecological Global Properties , 2006 .

[9]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  Dietmar Heinke,et al.  Selective Attention for Identification Model: Simulating visual neglect , 2005, Comput. Vis. Image Underst..

[11]  Marc Levoy,et al.  High-speed videography using a dense camera array , 2004, CVPR 2004.

[12]  T. Chen,et al.  Geometry-assisted statistical modeling for face mosaicing , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  Kenichi Kanatani,et al.  Image mosaicing by stratified matching , 2004, Image Vis. Comput..

[14]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

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