A graphics hardware implementation of the generalized Hough transform for fast object recognition, scale, and 3D pose detection

Abstract

The generalized Hough transform constitutes a wellknown approach to object recognition and pose detection. To attain reliable detection results, however, a very large number of candidate object poses and scale factors need to be considered. We employ an inexpensive, consumer-market graphics-card as the "poor man's" parallel processing system. We describe the implementation of a fast and enhanced version of the generalized Hough transform on graphics hardware. Thanks to the high bandwidth of on-board texture memory, a single pose can be evaluated in less than 3 ms, independent of the number of edge pixels in the image. From known object geometry, our hardware-accelerated generalized Hough transform algorithm is capable of detecting an object's 3D pose, scale, and position in the image within less than one minute. A good pose estimation is even delivered in less than 10 seconds.