City Research Online

Abstract

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 regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies Random Forest (RF) to learn the mapping between the estimated, noisy disparity and actual depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf node in each RF tree to identify features that are most discriminative for depth regression. Experimental results demonstrate the promise of the method to produce high quality depth images of a hand using an inexpensive stereo camera.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2016 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

Export

Downloads