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The purpose of this paper is to present a hierarchic processor architecture for the tracking of moving objects. Two goals are envisaged: the definition of a moving window for the target tracking, and multiresolution segmentation needed for scale independent target recognition. Memory windows in single processor systems obtained by software methods are limited in speed for high complexity images. In a multiprocessor system the limitation arises in bus or memory bottleneck. Highly concurrent system architectures have been studied and implemented as crossbar bus systems, multiple buses systems, or hypercube structures. Because of the complexity of these architectures and considering the particularities of image signals we suggest a hierarchic architecture that reduces the number of connections preserving the flexibility and which is well adapted for multiresolution algorithm implementations. The hierarchy is a quadtree. The solution is in using switched bus and block memory partition (granular image memory organization). To organize such an architecture in the first stage, the moving objects are identified in the camera field and the adequate windows are defined. The system is reorganized such as the computing power is concentrated in these windows. Image segmentation and motion prediction are accomplished. Motion parameters are interpreted to adapt the windows and to dynamically reorganize the system. The estimation of the motion parameters is done over low resolution images (top of the pyramid). Multiresolution image representation has been introduced for picture transmission and for scene analysis. The pyramidal implementation was elaborated for the evaluation of the image details at various scales. The multiresolution pyramid is obtained by low pass filtering and subsampling the intermediate result. The technique is applied over a limited range of scale. The multiresolution representations, as a consequence, are close to scale invariance. In the mean time image representation by wavelets allow scale to be implicit, that is why the wavelet transform is well adapted to evaluate the self similarity of the signals. The self similarity is the common point of wavelets and fractal signals. It is assumed that an image (signal) has fractal behavior if at several scales its `features'' show deterministic or statistical self-similarity or self-affinity. Texture analysis can be accomplished by fractal transform: each pixel of the original image is substituted by the value of the fractal dimension in its neighborhood. To evaluate the fractal dimension several techniques have been developed. It is necessary to compute the dimension of the set of points in the neighborhood of interest for a given range of resolutions. The slope of the approximated straight line in log/log plot of these values versus the unit of each scale is in linear dependence to the fractal dimension. It has been proven that fractal dimension can be evaluated from the ratio of the energies of the detail images in a multiresolution pyramid obtained by wavelet transform.
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