A Shape Classifier by Using Image Projection and a Neural Network

In this research a computer vision-based shape recognition system which combines an image projection and the back propagation neural network is proposed. The system uses the image projection and features of the projection histogram to represent the shape, and uses the back propagation neural network to classify the shape. In this research, a fast normalization scheme is developed which is especially designed for image projection and can also find the same projection axis no matter what the object's orientation. Therefore, the proposed system can be invariant to scale, translation, and orientation variations. This research finds that the direct representation scheme of the back propagation neural net has better accuracy than the binary coding representation scheme. This research also finds that the proposed system's classification accuracy is greater than 98.8% and its processing speed is also very fast. Therefore, the proposed system is feasible for use in shape classification, in practice.

[1]  Uwe Helmke,et al.  Existence and uniqueness results for neural network approximations , 1995, IEEE Trans. Neural Networks.

[2]  J. Mantas,et al.  Methodologies in pattern recognition and image analysis - A brief survey , 1987, Pattern Recognit..

[3]  David Casasent,et al.  Neural net selection of features for defect inspection , 1991, Other Conferences.

[4]  Takashi Tanaka,et al.  A neural network recognition system for machine printed characters on coils , 1995, IAS '95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting.

[5]  H.S. Cho,et al.  Visual inspection scheme for use in optical solder joint inspection system , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[6]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[7]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[8]  Kwanghee Nam,et al.  Object recognition of one-DOF tools by a back-propagation neural net , 1995, IEEE Trans. Neural Networks.

[9]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[10]  Mandyam D. Srinath,et al.  Contour sequence moments for the classification of closed planar shapes , 1987, Pattern Recognit..

[11]  A. Amin,et al.  Hand-printed character recognition system using artificial neural networks , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[12]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[13]  Mario Vento,et al.  A neural network classifier for OCR using structural descriptions , 1995 .

[14]  Yuan R. Wang,et al.  Characterization of Binary Patterns and Their Projections , 1975, IEEE Transactions on Computers.

[15]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[16]  Mohammad R. Sayeh,et al.  A neural network approach to robust shape classification , 1990, Pattern Recognit..

[17]  King-sun Fu,et al.  Pattern Recognition for Automatic Visual Inspection , 1982, Computer.