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Description:
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For humans , retinal images provide sufficient information for the complete understanding of three -dimensional (3 -D ) shapes in a scene . The ultimate goal of computer vision is to develop an automated system able to reproduce some of the tasks performed in a natural way by human beings as recognition , classification , or analysis of the environment as basis for further decisions . At the first level , referred to as early computer vision , the task is to extract symbolic descriptive information in a scene from a variety of sensory data .
The second level is concerned with classification , recognition , or decision systems and the related heuristics , that aid the processing of the available information .
This research is concerned with a new approach to 3 -D object representation and recognition using an interpolation scheme applied to the information from the fusion of range and intensity data . The range image acquisition uses a methodology based on a passive stereo -vision model originally developed to be used with a sequence of images .^^ However , curved features , large disparities and noisy input images are some of the problems associated with real imagery , which need to be addressed prior to applying the matching techniques in the spatial frequency domain . Some of the above mentioned problems can only be solved by computationally intensive spatial domain algorithms . Regularization techniques are explored for surface recovery from sparse range data , and intensity images are incorporated in the final representation of the surface . As an important application , the problem of 3 -D representation of retinal images for extraction of quantitative information is addressed .
Range information is also combined with intensity data to provide a more accurate numerical description based on aspect graphs . This representation is used as input to a three -dimensional object recognition system . Such an approach results in an improved performance of 3 -D object classifiers . |