NDLI: Learning generative models of scene features

Content Provider	IEEE Xplore Digital Library
Author	Sim, R. Dudek, G.
Copyright Year	2001
Description	Author affiliation: Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada (Sim, R.; Dudek, G.)
Abstract	We present a method for learning a set of generative models which are suitable for representing variations of selected image-domain features of the scene as a function of changes in the camera viewpoint. Such models are important for robotic tasks, such as probabilistic position estimation (i.e. localization), as well as visualization. Our approach entails the selection of image-domain features, as well as the synthesis of models of their visual behavior. The model we propose is capable of generating maximum likelihood views of automatically selected features, as well as a measure of the likelihood of a particular view from a particular camera position. Training the models involves regularizing observations of the features from known camera locations. The uncertainty of the model is evaluated using cross validation. The features themselves are initially selected automatically as salient points by a measure of visual attention, and are tracked across multiple views. While the motivation for this work is for robot localization, the results have implications for image interpolation, virtual scene reconstruction and object recognition. The paper presents a formulation of the problem and illustrative experimental results.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Pattern Analysis & Machine Intelligence
File Size	915964
File Format	PDF
ISBN	0769512720
ISSN	10636919
DOI	10.1109/CVPR.2001.990504
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Cameras Robot vision systems Robotics and automation Visualization Maximum likelihood estimation Position measurement Particle measurements Robot localization Interpolation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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