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We present an approach for unsupervised computation of local shape descriptors, which relies on the use of linear autoencoders for characterizing local regions of complex shapes. The proposed approach responds to the need for a robust scheme to index binary images using local descriptors, which arises when only few examples of the complete images are available for training, thus making inaccurate...
In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as shape encoding receptive fields (SERF), is able to perform fast and accurate data classification and regression of multi-dimensional data. A SERF is a histogram structure that encodes the shape of multi-dimensional data relative to its center,...
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