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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, SunokMin, DongboKim, SeungryongSohn, Kwanghoon
Ewha Authors
민동보
Issue Date
2017
Journal Title
IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN
1057-7149JCR Link

1941-0042JCR Link
Citation
IEEE TRANSACTIONS ON IMAGE PROCESSING vol. 26, no. 12, pp. 6019 - 6033
Keywords
Confidence measureconfidence feature augmentationconfidence map aggregationground control pointrandom regression forest
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Indexed
SCI; SCIE; SCOPUS WOS
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:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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