Real-time line detection through an improved Hough transform voting scheme
Pattern Recognition
On the computation of the Circle Hough Transform by a GPU rasterizer
Pattern Recognition Letters
Recognition of circular patterns on GPUs: Performance analysis and contributions
Journal of Parallel and Distributed Computing
Using Graphics Hardware for Enhancing Edge and Circle Detection
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
CAD-based recognition of 3D objects in monocular images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
International Journal of High Performance Computing Applications
GPU accelerated image processing for lip segmentation
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Real-time detection of lines using parallel coordinates and OpenGL
Proceedings of the 27th Spring Conference on Computer Graphics
Resource-efficient FPGA architecture and implementation of hough transform
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Real-time detection of lines using parallel coordinates and CUDA
Journal of Real-Time Image Processing
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The generalized Hough transform constitutes a well-known 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. In this paper 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 delivered in even less than 10 seconds.