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A common problem in manufacturing design is the accurate rendering of a 3-D object into a digitized 3-D representation. Such a rendering can be used to duplicate or modify existing objects as well as perform quality control and object recognition in automated manufacturing operations. There are several automated ways to retrieve 3-D information. If we limit our discussion to non-destructive computer vision techniques, these techniques include stereo- vision, orthogonal silhouette projection, gray level analysis, focal plane analysis, interferometric analysis, and structured light projection. In this paper we present a sine section structured light technique. This technique requires only one image of a surface area to process the topology, it is insensitive to reflected intensity variations and it can be accomplished with incoherent white light so it is insensitive to phase distortions caused by roughness. The sine section technique projects a sine wave image onto an object and recovers the local slopes (1st order) which are then combined to reconstruct the surface topology. This technique has the advantage over single slit structured light approaches which use position (0 order) information because it uses the entire area of a 2-D image as information for reconstruction. It has the advantage over multiple slits because, locally, it is the narrow-band frequency modulation of a sine wave. This decreases the side loop response. However globally it is a wide-band modulating technique (0 to infinite frequency) so an optimum frequency demodulation technique is developed which yields average slopes in local regions of the illuminated area. The performance of this technique appears to be very robust and insensitive to intensity- variations. It is also compatible with certain single slit projection techniques for 0 order recovery. In addition to this it yields local carrier frequencies which can be used with (2nd level processing) narrow-band phase demodulation techniques to recover higher resolution of surface variation. Results are presented for recovery of a second order surface corrupted by additive colored Gaussian spatial noise. The colored noise is generated with a fractal filter based on a fractal dimension parameter (beta) .
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