Distance Metric between 3 D Models and 2 D Image _ _ for Recognition and Classification D TIC

Abstract C Similarity measurements between 3D objects and 2D images are useful for the tasks of object recognition and classification. We distinguish between two types of similarity metrics: metrics computed in image-space (image metrics) and metrics computed in transformationspace (transformation metrics). Existing methods typically use image metrics; namely, metrics that measure the difference in the image between the observed image and the nearest view of the object. Example for such a measure is the Euclidean distance between feature points' in the image and their corresponding points in the nearest view. (Computing this measure i equivalent to solving the ezterior orientation calibration problem.) In this paper we introduce a, different type of metrics: transformation metrics. These metrics penalize for the deformations applied to the object to produce the observed image.

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