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Conditional Regressive Random Forest Stereo-based Hand Depth Recovery

Basaru, R. R., Child, C. H. T., Alonso, E. & Slabaugh, G. G. (2018). Conditional Regressive Random Forest Stereo-based Hand Depth Recovery. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 614-622. doi: 10.1109/ICCVW.2017.78


This paper introduces Conditional Regressive Random Forest (CRRF), a novel method that combines a closed-form Conditional Random Field (CRF), using learned weights, and a Regressive Random Forest (RRF) that employs adaptively selected expert trees. CRRF is used to estimate a depth image of hand given stereo RGB inputs. CRRF uses a novel superpixel-based regression framework that takes advantage of the smoothness of the hand’s depth surface. A RRF unary term adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. CRRF also includes a pair-wise term that encourages smoothness between similar adjacent superpixels. Experimental results show that CRRF can produce high quality depth maps, even using an inexpensive RGB stereo camera and produces state-of-the-art results for hand depth estimation.

Publication Type: Article
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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