Image Texture Analysis Techniques - a Survey
Image Texture Analysis Techniques - a Survey
Reasoning about Uncertainty
TAO-robust backpropagation learning algorithm
Neural Networks
Distance and nearest neighbor transforms on gray-level surfaces
Pattern Recognition Letters
Texture analysis methods for tool condition monitoring
Image and Vision Computing
A neural network-based approach for optimising rubber extrusion lines
International Journal of Computer Integrated Manufacturing
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This work presents a method to perform a surface finish control using a computer vision system. Test parts used were made of AISI 303 stainless steel and were machined with a MUPEM CNC multi-turret parallel lathe. Using a Pulnix PE2015 B/W camera, a diffuse illumination and a industrial zoom, 140 images were acquired. We have applied a vertical Prewitt filter to all the images obtaining two sets, the original one and the filtered. We have described the images using three different methods. The first features vector was composed by the mean, standard deviation, skewness and kurtosis of the image histogram. The second features vector was made up by four Haralick descriptors --- contrast, correlation, energy and homogeneity. The last one was composed by 9 Laws descriptors. Using k-nn we have obtained a hit rate around 90 % with filtered images and, the best one, using Laws features vector of 92.14% with unfiltered images. These results show that it is feasible to use texture descriptors to evaluate the rugosity of metallic parts in the context of product quality inspection.