Stochastic models for recognition of articulated objects

Author

Bhanu, Bir ; Tian, Bing

Author_Institution

Coll. of Eng., California Univ., Riverside, CA, USA

Volume

2

fYear

1997

fDate

26-29 Oct 1997

Firstpage

847

Abstract

We present a hidden Markov modeling (HMM) based approach for recognition of articulated objects in synthetic aperture radar (SAR) images. We develop multiple models for a given SAR image of an object and integrate these models synergistically using their probabilistic estimates for recognition and estimates of invariance of features as a result of articulation. The models are based on sequentialization of scattering centers extracted from SAR images. Experimental results are presented using 1440 training images and 2520 testing images for 4 classes

Keywords

feature extraction; hidden Markov models; image sequences; maximum likelihood estimation; object recognition; probability; radar cross-sections; radar imaging; radar target recognition; stochastic processes; synthetic aperture radar; 1D sequences; HMM; SAR images; articulated objects recognition; experimental results; feature invariance estimation; hidden Markov modeling; maximum likelihood decision; multiple models; probabilistic estimates; scattering centers; stochastic models; synthetic aperture radar; testing images; training image; Azimuth; Geometry; Hidden Markov models; Image recognition; Object recognition; Radar scattering; Solid modeling; Speech recognition; Stochastic processes; Synthetic aperture radar; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1997. Proceedings., International Conference on

Conference_Location

Santa Barbara, CA

Print_ISBN

0-8186-8183-7

Type

conf

DOI

10.1109/ICIP.1997.638629

Filename

638629