Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2013-07, Vol.23 (7), p.1119-1130
Main Authors: Pham, Cuong Cao, Jeon, Jae Wook
Format: Article
Language:English
Subjects:
Accuracy
Applied sciences
Artificial intelligence
Computational complexity
Computed tomography
Computer science
control theory
systems
Cost aggregation
domain transformation
Equations
Exact sciences and technology
Image color analysis
Image edge detection
Image processing
Information, signal and communications theory
local stereo matching
Measurement
Pattern recognition. Digital image processing. Computational geometry
Signal processing
Telecommunications and information theory
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Summary:Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem of these algorithms since the computational complexity is proportionally related to the support window size. In this paper, we present a novel cost aggregation method inspired by domain transformation, a recently proposed dimensionality reduction technique. This transformation enables the aggregation of 2-D cost data to be performed using a sequence of 1-D filters, which lowers computation and memory costs compared to conventional 2-D filters. Experiments show that the proposed method outperforms the state-of-the-art local methods in terms of computational performance, since its computational complexity is independent of the input parameters. Furthermore, according to the experimental results with the Middlebury dataset and real-world images, our algorithm is currently one of the most accurate and efficient local algorithms.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2012.2223794