Abstract

Compressive imaging employs the direct measurement of object features and has been shown to offer both performance (e.g., improved reconstructed image fidelity) and cost (e.g., reduced number of measurements relative to the native dimensionality) advantages. We examine compressive imaging within a stereo vision application in which a traditional correspondence algorithm is used to find pixel disparity maps. Through simulation we show that compressive imaging provides sufficient image fidelity with 12.8× compression to compute disparity maps with less that 4.5% error on average at 0.5% relative measurement noise strength.

© 2013 Optical Society of America

PDF Article
More Like This
Computer-generated holograms using stereo disparity with a multi-matching algorithm

Yan-Ling Piao, Ki-Chul Kwon, Jeong-Hyeon Lee, Sang-Keun Gil, and Nam Kim
27P_101 Conference on Lasers and Electro-Optics/Pacific Rim (CLEOPR) 2015

Object-Based Stereo Panorama Disparity Adjusting

Chiao Wang and Alexander A. Sawchuk
FWT6 Frontiers in Optics (FiO) 2006

Compressive temporal stereo-vision imaging

Xin Yuan, Yangyang Sun, and Shuo Pang
JT3A.30 3D Image Acquisition and Display: Technology, Perception and Applications (3D) 2016

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription