A Skeleton and Neural Network-Based Approach for Identifying Cosmetic Surface Flaws

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 back propagation 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.