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17 January 1985 3-D Object Recognition From a Single Image
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Proceedings Volume 0521, Intelligent Robots and Computer Vision; (1985)
Event: 1984 Cambridge Symposium, 1984, Cambridge, United States
A conceptually new algorithm for 3-D object recognition and shape estimation from a single image is presented. Here complex 3-D objects are viewed as concatenation of simple surfaces, essentially planar, cylindrical, and spherical. This paper addresses the problem of recognizing these different surfaces along with estimating their shape parameters from a single image. The surfaces are assumed to be Lambertian illuminated with a point source at infinity, and we allow more than one surface to exist in the image. Surface classification and recognition relies on exploiting the contours of constant image intensity associated with each surface. By Lambert's law the image intensity for a plane is just a constant; for a cylinder the contours are lines parallel to the axis of the cylinder, whereas for a sphere they are concentric circles (ellipses). The image is partitioned into small square blocks. In each the data patch could either be classified as planar or nonplanar (cylindrical). That involves looking at whether or not the ratio of the 2 eigenvalues of the scatter matrix associated with the contours is close to 1. For nonplanar (cylindrical) blocks the angle 0 associated with the direction of the lines is computed. Any nonplanar block is classified as cylinder or sphere by considering the distribution of the angles 0 in a 3x3 neighborhood centered around the block. Again based on the classification of the blocks (surface type) as well as their direction (0), the image is segmented into connected regions. Once a surface region is extracted, shape estimation is achieved.
© (1985) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fernand S. Cohen and Jean Francois P. Cayula "3-D Object Recognition From a Single Image", Proc. SPIE 0521, Intelligent Robots and Computer Vision, (17 January 1985);


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