A Graph-Based Approach for Shape Skeleton Analysis
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Shape skeleton classification using graph and multi-scale fractal dimension
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Skeleton representation of character based on multiscale approach
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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This paper introduces an approach to cosmetic surface flaw identification that is essentially invariant to changes in workpiece orientation and position while being efficient in the use of computer memory. Visual binary images of workpieces are characterized according to the number of pixels in progressive subskeleton iterations. Those subskeletons are constructed using a modified Zhou skeleton transform with disk shaped structuring elements. Two coding schemes are proposed to record the pixel counts of succeeding subskeletons with and without lowpass filtering. The coded pixel counts are on-line fed to a supervised neural network that is previously trained by the backpropagation method using flawed and unflawed simulation patterns. The test workpiece is then identified as flawed or unflawed by comparing its coded pixel counts to associated training patterns. Such off-line trainings using simulated patterns avoid the problems of collecting flawed samples. Since both coding schemes tremendously reduce the representative skeleton image data, significant run time in each epoch is saved in the application of neural networks. Experimental results are reported using six different shapes of workpieces to corroborate the proposed approach