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The original algorithm of Hough transform was for detecting and finding straight lines in two dimensional images. Since then, it has been extended to detect and locate special planar curves such as circles, parabolas, etc. Recently, generalized Hough transform has been applied for recognizing and locating three dimensional objects using range data. Our work is based on the same principle by decomposing the overall parameter set of nine elements representing a quadric into three decoupled parameter subsets: scalar parameter, translational parameter, and orientational parameters. The use of Hough transform on each decoupled subset in an independent but sequential manner rather than the whole enables us to reduce the computational cost and storage requirements by orders of magnitude as compared to all previous published applications.