NDLI: Data-driven recovery of hand depth using CRRF on stereo images

Content Provider	IET Digital Library
Author	Basaru, Rilwan Remilekun Child, Chris Alonso, Eduardo Slabaugh, Gregory
Abstract	Hand pose is emerging as an important interface for human–computer interaction. This study 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, the authors introduce conditional regressive random forest (CRRF), a method that combines a conditional random field (CRF) and an RRF to model the mapping from a stereo red, green and blue 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 superpixel independently, the CRF unifies the prediction of depth by modelling pairwise 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.
Starting Page	666
Ending Page	678
Page Count	13
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 5, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0227
Journal	IET Computer Vision
Publisher Date	2018-02-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adjacent Superpixels Blue Image Pair Camera Computer Vision And Image Processing Technique Conditional Random Field Conditional Regressive Random Forest CRF CRRF Data-driven Method Depth Image Depth Prediction Depth Surface Different Stereo-matching Measures Green Image Pair Hand Depth Hand Pose High-quality Depth Map Human-computer Interaction Image Classification Image Matching Image Segmentation Important Interface Inexpensive Stereo Camera Novel Superpixel Optical, Image And Video Signal Processing Pairwise Interactions Pose Estimation Regression Analysis Regression Framework RRF Statistics Stereo Image Stereo Image Processing Stereo Red Image Pair Stereoscopic Camera Input
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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