Issue No. 08 - August (1994 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.308474
<p>Recovering the 3-D shape of an object from its 2-D image contour is an important problem in computer vision. In this correspondence, the author motivates and develops two object-based heuristics. The structured nature of objects is the motivation for the nonaccidental alignment criterion: parallel coordinate axes within the object's bounding contour correspond to object-centered coordinate axes. The regularity and symmetry inherent in many man-made objects is the motivation for the orthogonal basis constraint. An oblique set of coordinate axes in the image is presumed to be the projection of an orthogonal set of 3-D coordinate axes in the scene. These object-based heuristics are used to recover shape in both real and synthetic images.</p>
computer vision; image recognition; object-based heuristics; 3-D shape recovery; 2-D image contour; computer vision; nonaccidental alignment criterion; bounding contour; parallel coordinate axes; object-centered coordinate axes; regularity; symmetry; man-made objects; orthogonal basis constraint; synthetic images; real images
A. Gross, "Toward Object-Based Heuristics," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 794-802, 1994.