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Data-driven Recovery of Hand Depth using Conditional Regressive Random Forest on Stereo Images

Basaru, R. R., Slabaugh, G. G., Alonso, E. & Child, C. H. T. (2018). Data-driven Recovery of Hand Depth using Conditional Regressive Random Forest on Stereo Images. IET Computer Vision, 12(5), pp. 666-678. doi: 10.1049/iet-cvi.2017.0227


Hand pose is emerging as an important interface for human-computer interaction. This paper presents a data-driven method to estimate a high-quality depth map of a hand from a stereoscopic camera input by introducing a novel superpixel based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, we introduce Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels. Experimental results show that CRRF can generate a depth image more accurately than the leading contemporary techniques using an inexpensive stereo camera.

Publication Type: Article
Additional Information: This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.
Departments: School of Science & Technology > Computer Science
Text - Accepted Version
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