NDLI: Object Localisation Using Generative Probability Model for Spatial Constellation and Local Image Features

Content Provider	IEEE Xplore Digital Library
Author	Kamarainen, J.-K. Hamouz, M. Kittler, J. Paalanen, P. Ilonen, J. Drobchenko, A.
Copyright Year	2007
Description	Author affiliation: Univ. of Surrey, Guildford (Kamarainen, J.-K.; Hamouz, M.; Kittler, J.)
Abstract	In this paper we apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probability model. In contrast to the recent semi- supervised methods we do not utilise interest point detectors, but apply a supervised approach where local image features (landmarks) are annotated in a training set and therefore their appearance and spatial variation can be learnt. Our method enables working in purely probabilistic search spaces providing a MAP estimate of object location, and in contrast to the recent methods, no background class needs to be formed. Using the training set we can estimate pdfs for both spatial constellation and local feature appearance. By applying an inference bias that the largest pdf mode has probability one, we are able to combine prior information (spatial configuration of the features) and observations (image feature appearance) into posterior distribution which can be generatively sampled, e.g. using MCMC techniques. The MCMC methods are sensitive to initialisation, but as a solution, we also propose a very efficient and accurate RANSAC-based method for finding good initial hypotheses of object poses. The complete method can robustly and accurately detect and localise objects under any homography.
Starting Page	1
Ending Page	8
File Size	907328
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4409186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Robustness Solid modeling Speech processing Signal processing Machine vision Pattern recognition Signal generators Detectors Phase detection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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