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Sensors 2015, 15(5), 10118-10145; doi:10.3390/s150510118

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

1
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA
2
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
3
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 13 February 2015 / Revised: 17 April 2015 / Accepted: 22 April 2015 / Published: 29 April 2015
(This article belongs to the Special Issue Sensors in New Road Vehicles)
View Full-Text   |   Download PDF [3060 KB, uploaded 29 April 2015]   |  

Abstract

We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation). View Full-Text
Keywords: infrared ATR; level set; shape modeling; particle swarm optimization infrared ATR; level set; shape modeling; particle swarm optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, L.; Fan, G.; Gong, J.; Havlicek, J.P. Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set. Sensors 2015, 15, 10118-10145.

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