NDLI: A robust shape model for multi-view car alignment

Content Provider	IEEE Xplore Digital Library
Author	Yan Li Gu, L. Kanade, T.
Copyright Year	2009
Description	Author affiliation: Carnegie Mellon Univ., Pittsburgh, PA, USA (Yan Li; Gu, L.; Kanade, T.)
Abstract	We present a robust shape model for localizing a set of feature points on a 2D image. Previous shape alignment models assume Gaussian observation noise and attempt to fit a regularized shape using all the observed data. However, such an assumption is vulnerable to gross feature detection errors resulted from partial occlusions or spurious background features. We address this problem by using a hypothesis-and-test approach. First, a Bayesian inference algorithm is developed to generate object shape and pose hypotheses from randomly sampled partial shapes - subsets of feature points. The hypotheses are then evaluated to find the one that minimizes the shape prediction error. The proposed model can effectively handle outliers and recover the object shape. We evaluate our approach on a challenging dataset which contains over 2,000 multi-view car images and spans a wide variety of types, lightings, background scenes, and partial occlusions. Experimental results demonstrate favorable improvements over previous methods on both accuracy and robustness.
Starting Page	2466
Ending Page	2473
File Size	4021983
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206799
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Active shape model Shape measurement Bayesian methods Noise shaping Noise robustness Gaussian noise Inference algorithms Multi-stage noise shaping Computer vision Layout
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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