NDLI: On Stereo Confidence Measures for Global Methods: Evaluation, New Model and Integration into Occupancy Grids

Content Provider	IEEE Xplore Digital Library
Author	Brandao, M. Ferreira, R. Hashimoto, K. Takanishi, A. Santos-Victor, J.
Copyright Year	1979
Abstract	Stereo confidence measures are important functions for global reconstruction methods and some applications of stereo. In this article we evaluate and compare several models of confidence which are defined at the whole disparity range. We propose a new stereo confidence measure to which we call the Histogram Sensor Model (HSM), and show how it is one of the best performing functions overall. We also introduce, for parametric models, a systematic method for estimating their parameters which is shown to lead to better performance when compared to parameters as computed in previous literature. All models were evaluated when applied to two different cost functions at different window sizes and model parameters. Contrary to previous stereo confidence measure benchmark literature, we evaluate the models with criteria important not only to winner-take-all stereo, but also to global applications. To this end, we evaluate the models on a real-world application using a recent formulation of 3D reconstruction through occupancy grids which integrates stereo confidence at all disparities. We obtain and discuss our results on both indoors' and outdoors' publicly available datasets.
Sponsorship	IEEE Computer Society
Starting Page	116
Ending Page	128
Page Count	13
File Size	1119885
File Format	PDF
ISSN	01628828
Volume Number	38
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Cost function Stereo vision Histograms Benchmark testing Measurement uncertainty Time measurement occupancy grids stereo matching confidence uncertainty 3D reconstruction
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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