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Joint infrared target recognition and segmentation using a shape manifold-aware level set.

Yu L, Fan G, Gong J, Havlicek JP - Sensors (Basel) (2015)

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA. liangjiang.yu@okstate.edu.

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).

No MeSH data available.


Related in: MedlinePlus

Effectiveness of the energy function. (Left) Plot of the energy function with respect to the identity latent variable; (right) a group of CVIM-generated shapes corresponding to the latent variables labeled in the plot on the left are superimposed onto the original IR data chips.
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f6-sensors-15-10118: Effectiveness of the energy function. (Left) Plot of the energy function with respect to the identity latent variable; (right) a group of CVIM-generated shapes corresponding to the latent variables labeled in the plot on the left are superimposed onto the original IR data chips.

Mentions: Optimizing the posterior Equation (2) may be thought of as finding a contour that maximizes the histogram difference between the foreground and background in the region of interest. This consideration is based on the assumption that the target-of-interest has different intensity values compared with the background. Intuitively, if the shape contour of the target is correctly hypothesized in terms of the target type (recognition), view angle (pose estimation) and location (segmentation), then the foreground and background defined by this contour will have maximum histogram divergence and, therefore, maximize the energy function Equation (2) as illustrated in Figure 6. For a given observation, in Figure 6, we calculate the value of the energy function with respect to almost all possible values along the circularly-shaped identity manifold α = 1, 2, 3, …, 360° with the view angle Θ known for simplicity. The figure shows several CVIM interpolated shapes superimposed on the original mid-wave IR image data. As seen in the left part of the figure, the maximum value of the energy function is attained by the contour (numbered 4) that is best in the sense of being closest to the actual boundary of the target in the right part of the figure. However, the multi-model nature of the energy function as shown in Figure 6 (left part), which is typical, represents significant challenges for CVIM-based shape optimization and motivates the PSO and GB-PSO algorithms that we develop below in Sections 3.3 and 3.4.


Joint infrared target recognition and segmentation using a shape manifold-aware level set.

Yu L, Fan G, Gong J, Havlicek JP - Sensors (Basel) (2015)

Effectiveness of the energy function. (Left) Plot of the energy function with respect to the identity latent variable; (right) a group of CVIM-generated shapes corresponding to the latent variables labeled in the plot on the left are superimposed onto the original IR data chips.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4481947&req=5

f6-sensors-15-10118: Effectiveness of the energy function. (Left) Plot of the energy function with respect to the identity latent variable; (right) a group of CVIM-generated shapes corresponding to the latent variables labeled in the plot on the left are superimposed onto the original IR data chips.
Mentions: Optimizing the posterior Equation (2) may be thought of as finding a contour that maximizes the histogram difference between the foreground and background in the region of interest. This consideration is based on the assumption that the target-of-interest has different intensity values compared with the background. Intuitively, if the shape contour of the target is correctly hypothesized in terms of the target type (recognition), view angle (pose estimation) and location (segmentation), then the foreground and background defined by this contour will have maximum histogram divergence and, therefore, maximize the energy function Equation (2) as illustrated in Figure 6. For a given observation, in Figure 6, we calculate the value of the energy function with respect to almost all possible values along the circularly-shaped identity manifold α = 1, 2, 3, …, 360° with the view angle Θ known for simplicity. The figure shows several CVIM interpolated shapes superimposed on the original mid-wave IR image data. As seen in the left part of the figure, the maximum value of the energy function is attained by the contour (numbered 4) that is best in the sense of being closest to the actual boundary of the target in the right part of the figure. However, the multi-model nature of the energy function as shown in Figure 6 (left part), which is typical, represents significant challenges for CVIM-based shape optimization and motivates the PSO and GB-PSO algorithms that we develop below in Sections 3.3 and 3.4.

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA. liangjiang.yu@okstate.edu.

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).

No MeSH data available.


Related in: MedlinePlus