TY  - JOUR
T1  - Stereo Matching Performance Analysis of Cost Functions on the Graphic Processing Unit (GPU) for Pervasive Computing
AU - Hong, Gwang-Soo AU - Hoe, Woong AU - Kim, Byung-Gyu AU - Beak, Jang-Woon AU - Kwon, Kee-Koo 
JO  - Journal of Engineering and Applied Sciences
VL  - 11
IS  - 7
SP  - 1480
EP  - 1487
PY  - 2016
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2016.1480.1487
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2016.1480.1487
KW  - Stereo vision
KW  -CUDA
KW  -GPU
KW  -cost function
KW  -stereo matching
KW  -pervasive computing
AB  - Stereo imaging is a powerful technique fordetermining the distance to objects using a pairs of cameraspaced apart. The extremely high computational requirements ofstereo vision limit application to non realtime applications wherehigh computing power is available. To overcome the limitation, we utilized the general strategy for parallelization of dense cost functions on Compute Unified Device Architecture (CUDA) with Graphic Processing Unit (GPU), especially for pervasive environment. The challenges of mapping a sequential stereo matching algorithm to a massively parallel thread environment are considered. Compared to the CPU counterpart, the processing speed of the stereo matching algorithm based on CUDA programming can be improved by about from 107-369 times.
ER  -