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Feature Augmentation for Learning Confidence Measure in Stereo Matching
- Title
- Feature Augmentation for Learning Confidence Measure in Stereo Matching
- Authors
- Kim, Sunok; Min, Dongbo; Kim, Seungryong; Sohn, Kwanghoon
- Ewha Authors
- 민동보
- SCOPUS Author ID
- 민동보
- Issue Date
- 2017
- Journal Title
- IEEE TRANSACTIONS ON IMAGE PROCESSING
- ISSN
- 1057-7149
1941-0042
- Citation
- IEEE TRANSACTIONS ON IMAGE PROCESSING vol. 26, no. 12, pp. 6019 - 6033
- Keywords
- Confidence measure; confidence feature augmentation; confidence map aggregation; ground control point; random regression forest
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Confidence estimation is essential for refining stereo matching results through a post-processing step. This problem has recently been studied using a learning-based approach, which demonstrates a substantial improvement on conventional simple non-learning based methods. However, the formulation of learning-based methods that individually estimates the confidence of each pixel disregards spatial coherency that might exist in the confidence map, thus providing a limited performance under challenging conditions. Our key observation is that the confidence features and resulting confidence maps are smoothly varying in the spatial domain, and highly correlated within the local regions of an image. We present a new approach that imposes spatial consistency on the confidence estimation. Specifically, a set of robust confidence features is extracted from each superpixel decomposed using the Gaussian mixture model, and then these features are concatenated with pixel-level confidence features. The features are then enhanced through adaptive filtering in the feature domain. In addition, the resulting confidence map, estimated using the confidence features with a random regression forest, is further improved through K-nearest neighbor based aggregation scheme on both pixel-and superpixel-level. To validate the proposed confidence estimation scheme, we employ cost modulation or ground control points based optimization in stereo matching. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on various benchmarks including challenging outdoor scenes.
- DOI
- 10.1109/TIP.2017.2750404
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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