Abstract
In the context of computer vision, the recognition of three-dimensional objects typically consists of image capture, feature extraction, and object model matching. During the image capture phase, a camera senses the brightness at regularly spaced points, or pixels, in the image. The brightness at these points is quantized into discrete values; the two-dimensional array of quantized values forms a digital image, the input to the computer vision system. During the feature extraction phase, various algorithms are applied to the digital image to extract salient features such as lines, curves, or regions. The set of these features, represented by a data structure, is then compared to the database of object model data structures in an attempt to identify the object. Clearly, the type of features that need to be extracted from the image depends on the representation of objects in the database.
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Dickinson, S.J., Pentland, A.P., Rosenfeld, A. (1990). A Representation for Qualitative 3-D Object Recognition Integrating Object-Centered and Viewer-Centered Models. In: Leibovic, K.N. (eds) Science of Vision. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3406-7_13
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DOI: https://doi.org/10.1007/978-1-4612-3406-7_13
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