NDLI: Linear model hashing and batch RANSAC for rapid and accurate object recognition

Content Provider	IEEE Xplore Digital Library
Author	Shan, Y. Matei, B. Sawhney, H.S. Kumar, R. Huber, D. Hebert, M.
Copyright Year	2004
Description	Author affiliation: Sarnoff Corp., Princeton, NJ, USA (Shan, Y.; Matei, B.; Sawhney, H.S.; Kumar, R.)
Abstract	This paper proposes a joint feature-based model indexing and geometric constraint based alignment pipeline for efficient and accurate recognition of 3D objects from a large model database. Traditional approaches either first prune the model database using indexing without geometric alignment or directly perform recognition based alignment. The indexing based pruning methods without geometric constraints can miss the correct models under imperfections such as noise, clutter and obscurations. Alignment based verification methods have to linearly verify each model in the database and hence do not scale up. The proposed techniques use spin images as semi-local shape descriptors and locality-sensitive hashing (LSH) to index into a joint spin image database for all the models. The indexed models represented in the pruned set are further pruned using progressively complex geometric constraints. A simple geometric configuration of multiple spin images, for instance a doublet, is first used to check for geometric consistency. Subsequently, full Euclidean geometric constraints are applied using RANSAC-based techniques on the pruned spin images and the models to verify specific object identity. As a result, the combined indexing and geometric alignment based pipeline is able to focus on matching the most promising models, and generate far less pose hypotheses while maintaining the same level of performance as the sequential alignment based recognition. Furthermore, compared to geometric indexing techniques like geometric hashing, the construction time and storage complexity for the proposed technique remains linear in the number of features rather than higher order polynomial. Experiments on a 56 3D model database show promising results.
Sponsorship	IEEE Comput. Soc
File Size	436785
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object recognition Solid modeling Indexing Spatial databases Pipelines Image databases Noise shaping Shape Indexes Polynomials
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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