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Constant and consistent quality levels in the manufacturing industry increasingly require automatic inspection. This paper describes a vision system for leather inspection based upon visual textural properties of the material surface. As visual appearances of both leather and defects exhibit a wide range of variations due to original skin characteristics, curing processes and defect causes, location and classification of defective areas become hard tasks. This paper describes a method for separating the oriented structures of defects from normal leather, a background not homogeneous in color, thickness, brightness and finally in wrinkledness. The first step requires the evaluation of the orientation field from the image of the leather. Such a field associates to each point of the image a 2D vector having as direction the dominant local orientation of gradient vectors and the length proportional to their coherence evaluated in a neighborhood of fixed size. The second step analyzes such a vector flow field by projecting it on a set of basis vectors (elementary texture vectors) spanning the vector space where the vector fields associated to the defects can be defined. The coefficients of these projections are the parameters by means of which both detection and classification can be performed. Since the set of basis vectors is neither orthogonal nor complete, the projection requires the definition of a global optimization criteria that has been chosen to be the minimum difference between the original flow field and the vector field obtained as a linear combination of the basis vectors using the estimated coefficients. This optimization step is performed through a neural network initialized to recognize a limited number of patterns (corresponding to the basis vectors). This second step estimates the parameter vector in each point of the original image. Both leather without defects and defects can be characterized in terms of coefficient vectors making it possible to devise a filter process that detects any abnormal part of leather. The resulting system does not depend on kind, dimension and color of defects and uses only local information (it can therefore be implemented in a parallel way for dealing with large pieces of leather). Finally it works also on different materials: it has been used successfully on wood and ferromagnetic materials.
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