JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
GPU-based Stereo Matching Algorithm with the Strategy of Population-based Incremental Learning |
| 영문제목(English Title) |
GPU-based Stereo Matching Algorithm with the Strategy of Population-based Incremental Learning |
| 저자(Author) |
Donghu Nie
Kyuphil Han
Hengsuk Lee
|
| 원문수록처(Citation) |
VOL 05 NO. 02 PP. 0105 ~ 0116 (2009. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
To solve the general problems surrounding the application
of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption and search inefficiency，
and a scheme for controlling the distance of neighbors for
disparity smoothness is inserted to obtain a
wide-area consistency of disparities. In addition, an
alternative version of the proposed algorithm,
without the use of a probability vector, is also presented
for simpler set-ups. Secondly, programmable
graphics-hardware (GPU) consists of multiple multi-
processors and has a powerful parallelism which
can perform operations in parallel at low cost. Therefore,
in order to decrease the running time further,
a model of the proposed algorithm, which can be run on
programmable graphics-hardware (GPU), is
presented for the first time. The algorithms are
implemented on the CPU as well as on the GPU and are
evaluated by experiments. The experimental results show
that the proposed algorithm offers better
performance than traditional BMA methods with a deliberate
relaxation and its modified version in
terms of both running speed and stability. The comparison
of computation times for the algorithm both
on the GPU and the CPU shows that the former has more
speed-up than the latter, the bigger the image
size is. |
| 키워드(Keyword) |
Image filtering
Performance Evaluation
General-Purpose Computation Based on GPU
GPU
Population-Based Incremental Learning
|
| 파일첨부 |
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