Shape discrimination using integral features

“Integral features” are calculated by summing the local features over all the pixels, where the local features are determined by the state of the neighborhood of each pixel. The states are defined by using a two-dimensional series of mask functions on the polar coordinate system with the logarithmic scale of r-direction. This definition enables the efficient extraction of features from any arbitrary distant area. The features for shape discrimination are constructed from the short-range correlations of the gradients of the image data. For discrimination of image data we used the linear model as used in the multivariate analysis. We also developed nonlinear model learning by maximizing the discriminant efficiency. In the models, each pixel has the value that represents the validity of discrimination and weighted summations are performed when the integral features are calculated. The validity of the linear and nonlinear models is verified in experiments using the image data of real objects