Refinement of matching costs for stereo disparities using recurrent neural networks
Abstract Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances be...
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Online Access: | https://doi.org/10.1186/s13640-021-00551-9 |
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doaj-94189f3ff51b417a91dd6180e5ac11622021-04-11T11:14:52ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812021-04-012021111910.1186/s13640-021-00551-9Refinement of matching costs for stereo disparities using recurrent neural networksAlper Emlek0Murat Peker1Department of Electrical and Electronics Engineering, Nigde Omer Halisdemir UniversityDepartment of Electrical and Electronics Engineering, Nigde Omer Halisdemir UniversityAbstract Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.https://doi.org/10.1186/s13640-021-00551-9Computer visionMulti-layer neural networksRecurrent neural networksStereo image processing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alper Emlek Murat Peker |
spellingShingle |
Alper Emlek Murat Peker Refinement of matching costs for stereo disparities using recurrent neural networks EURASIP Journal on Image and Video Processing Computer vision Multi-layer neural networks Recurrent neural networks Stereo image processing |
author_facet |
Alper Emlek Murat Peker |
author_sort |
Alper Emlek |
title |
Refinement of matching costs for stereo disparities using recurrent neural networks |
title_short |
Refinement of matching costs for stereo disparities using recurrent neural networks |
title_full |
Refinement of matching costs for stereo disparities using recurrent neural networks |
title_fullStr |
Refinement of matching costs for stereo disparities using recurrent neural networks |
title_full_unstemmed |
Refinement of matching costs for stereo disparities using recurrent neural networks |
title_sort |
refinement of matching costs for stereo disparities using recurrent neural networks |
publisher |
SpringerOpen |
series |
EURASIP Journal on Image and Video Processing |
issn |
1687-5281 |
publishDate |
2021-04-01 |
description |
Abstract Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets. |
topic |
Computer vision Multi-layer neural networks Recurrent neural networks Stereo image processing |
url |
https://doi.org/10.1186/s13640-021-00551-9 |
work_keys_str_mv |
AT alperemlek refinementofmatchingcostsforstereodisparitiesusingrecurrentneuralnetworks AT muratpeker refinementofmatchingcostsforstereodisparitiesusingrecurrentneuralnetworks |
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1721531292710862848 |