Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching

• 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
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2012.2223794

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.