Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Real-time stereo vision on the PARTS reconfigurable computer
FCCM '97 Proceedings of the 5th IEEE Symposium on FPGA-Based Custom Computing Machines
ROS-DMA: A DMA Double Buffering Method for Embedded Image Processing with Resource Optimized Slicing
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
The Tyzx DeepSea G2 Vision System, ATaskable, Embedded Stereo Camera
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pfelib: a performance primitives library for embedded vision
EURASIP Journal on Embedded Systems
Low-Cost Stereo Vision on an FPGA
FCCM '07 Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Comparison of nonparametric transformations and bit vector matching for stereo correlation
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Phase-Correlation Guided Search for Realtime Stereo Vision
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Distributed real-time stereo matching on smart cameras
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
A fast stereo matching algorithm suitable for embedded real-time systems
Computer Vision and Image Understanding
Accurate hardware-based stereo vision
Computer Vision and Image Understanding
Accurate 3D-vision-based obstacle detection for an autonomous train
Computers in Industry
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This paper presents S 3 E , a software implementation of a high-quality dense stereo matching algorithm. The algorithm is based on a Census transform with a large mask size. The strength of the system lies in the flexibility in terms of image dimensions, disparity levels, and frame rates. The program runs on standard PC hardware utilizing various SSE instructions. We describe the performance optimization techniques that had a considerably high impact on the run-time performance. Compared to a generic version of the source code, a speedup factor of 112 could be achieved. On input images of 320×240 and a disparity range of 30, S 3 E achieves 42fps on an Intel Core 2 Duo CPU running at 2GHz.