In this paper a new Distributed Neural Network Architecture (DNNA) for object recognition is presented. The proposed architecture is tested in two scenarios: occluded planar object recognition and face recognition. The DNNA is composed by several classifiers, each one with a standard ART2 Neural Network (ART2-NN) connected to a Memory Map (MM), a set of logical AND gates, an evidence register, and a set of comparators. In a first step, objects are described by a set of sub-feature vectors (SFV), during the training stage, each SFV is then fed to an ARR-NN to train it and to build its corresponding Memory Map (MM). During a second phase of indexing a new image possibly containing the object is used to retrieve fiom the previously constructed MM the list of candidate objects that are in the image. A selection threshold is finally used to select from this list the objects that most resemble the objects on the image.
In this paper an edge preserving lossy image coder is presented. An edge image is obtained from the original with a digital image processing module using four different filters: Canny, Sobel, Roberts and Prewitt, then the original image is domain transformed with wavelets or contourlets, and a pixel mapping from original domain to transformed is done. For the compression, the edges points and the approximation image (which determines the compression factor) are selected; finally the image is decompressed in order to observe the reconstruction quality and edge preserving. Additionally, the results obtained from comparisons of error measures between original and decompressed images are shown and finally conclusions about the coder are presented.
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