Abstract
Stereo matching algorithms are capable of generating depth maps from two images of the same scene taken simultaneously from two different viewpoints. Traditionally, a single cost function is used to calculate the disparity between corresponding pixels in the left and right images. In the present research, we have considered a combination of simple data costs. A new method to combine multiple data costs is presented and a fuzzy-based disparity selection method is proposed. Experiments with different combinations of parameters are conducted and compared through the Middlebury and Kitti Stereo Vision Benchmark.
| Original language | English |
|---|---|
| Pages (from-to) | 377-391 |
| Number of pages | 15 |
| Journal | Turkish Journal of Electrical Engineering and Computer Sciences |
| Volume | 27 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 01-01-2019 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Electrical and Electronic Engineering
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Fuzzy logic-based disparity selection using multiple data costs for stereo correspondence. / Shetty, Akhil Appu; George, Vadakekara Itty; Nayak, C. Gurudas; Shetty, Raviraj.
In: Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 27, No. 1, 01.01.2019, p. 377-391.Research output: Contribution to journal › Article
TY - JOUR
T1 - Fuzzy logic-based disparity selection using multiple data costs for stereo correspondence
AU - Shetty, Akhil Appu
AU - George, Vadakekara Itty
AU - Nayak, C. Gurudas
AU - Shetty, Raviraj
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Stereo matching algorithms are capable of generating depth maps from two images of the same scene taken simultaneously from two different viewpoints. Traditionally, a single cost function is used to calculate the disparity between corresponding pixels in the left and right images. In the present research, we have considered a combination of simple data costs. A new method to combine multiple data costs is presented and a fuzzy-based disparity selection method is proposed. Experiments with different combinations of parameters are conducted and compared through the Middlebury and Kitti Stereo Vision Benchmark.
AB - Stereo matching algorithms are capable of generating depth maps from two images of the same scene taken simultaneously from two different viewpoints. Traditionally, a single cost function is used to calculate the disparity between corresponding pixels in the left and right images. In the present research, we have considered a combination of simple data costs. A new method to combine multiple data costs is presented and a fuzzy-based disparity selection method is proposed. Experiments with different combinations of parameters are conducted and compared through the Middlebury and Kitti Stereo Vision Benchmark.
UR - http://www.scopus.com/inward/record.url?scp=85062996231&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062996231&partnerID=8YFLogxK
U2 - 10.3906/elk-1807-180
DO - 10.3906/elk-1807-180
M3 - Article
AN - SCOPUS:85062996231
VL - 27
SP - 377
EP - 391
JO - Turkish Journal of Electrical Engineering and Computer Sciences
JF - Turkish Journal of Electrical Engineering and Computer Sciences
SN - 1300-0632
IS - 1
ER -