Khaled Mahar
Artificial Neural Network For Texture Classification Using Several Features: A Comparative Study
texture analysis plays an essentiala major rule in image classificationsegmentation in a wide range of applications such as medical imaging, remote sensingindustrial inspection. in this paper, we review the well known approaches of texture feature extractionperform a comparative study between them. these approaches are namely gray level histogram, edge detection,co-occurrence matrices, besides gaborbiorthogonal wavelet transformations. the feed forward artificial neural network (ann) with back- propagation algorithm (bpa) is used as a supervised classifier. experiments are conducted on two different datasets taken from multi-class engineering surfaces produced by six machining processesfrom brodatz (1966) textures album respectively. the classification accuracy is tested for both datasets, while the quality of estimation is tested for surface roughness parameters of the machined surfaces dataset only based on the roughness parameters evaluated from a contact measurement test.