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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

Examples of target signature variability from the Military Sensing Information Analysis Center (SENSIAC) ATR database [5]. The first and second rows show diurnal and nocturnal mid-wave IR (MWIR) images of a BTR70 personnel carrier, respectively. The third and fourth rows are diurnal and nocturnal images of a T72 main battle tank. Targets in each column are under the same view.
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f1-sensors-15-10118: Examples of target signature variability from the Military Sensing Information Analysis Center (SENSIAC) ATR database [5]. The first and second rows show diurnal and nocturnal mid-wave IR (MWIR) images of a BTR70 personnel carrier, respectively. The third and fourth rows are diurnal and nocturnal images of a T72 main battle tank. Targets in each column are under the same view.

Mentions: The infrared ATR problem presents significant challenges. Growth and processing techniques for IR detector materials, such as HgCdTe and InSb, are less mature than those for silicon, and hence, imaging IR sensors are typically characterized by higher noise and poor uniformity compared to their visible wavelength counterparts. The imagery acquired under practical field conditions often exhibits strong, structured clutter, poor target-to-clutter ratios and poor SNR. In important surveillance, security and military applications, the targets of interest may be non-cooperative, employing camouflage, decoys, countermeasures and complex maneuvers in an effort to evade detection and tracking. These difficulties are often exacerbated by the strong ego-motion of the sensor platform relative to the target. Depending on the operational waveband of the sensor, environmental conditions, such as smoke, haze, fog and rain, can result in degraded target signatures, as well as partial or full occlusions. All of these factors contribute to substantial appearance variability of the target thermal signature observed by the sensor, thereby limiting the effectiveness of approaches based on, e.g., stored libraries of static a priori signatures. A few examples of MWIR signature variability from the Military Sensing Information Analysis Center (SENSIAC) ATR Algorithm Development Image Database [5] are shown in Figure 1. Moreover, one would ideally like the ATR system to be capable of generalizing on the fly, so that both unknown target types and previously unseen views of known target types can be detected, tracked and recognized, at least to within an appropriate target class.


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

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

Examples of target signature variability from the Military Sensing Information Analysis Center (SENSIAC) ATR database [5]. The first and second rows show diurnal and nocturnal mid-wave IR (MWIR) images of a BTR70 personnel carrier, respectively. The third and fourth rows are diurnal and nocturnal images of a T72 main battle tank. Targets in each column are under the same view.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-15-10118: Examples of target signature variability from the Military Sensing Information Analysis Center (SENSIAC) ATR database [5]. The first and second rows show diurnal and nocturnal mid-wave IR (MWIR) images of a BTR70 personnel carrier, respectively. The third and fourth rows are diurnal and nocturnal images of a T72 main battle tank. Targets in each column are under the same view.
Mentions: The infrared ATR problem presents significant challenges. Growth and processing techniques for IR detector materials, such as HgCdTe and InSb, are less mature than those for silicon, and hence, imaging IR sensors are typically characterized by higher noise and poor uniformity compared to their visible wavelength counterparts. The imagery acquired under practical field conditions often exhibits strong, structured clutter, poor target-to-clutter ratios and poor SNR. In important surveillance, security and military applications, the targets of interest may be non-cooperative, employing camouflage, decoys, countermeasures and complex maneuvers in an effort to evade detection and tracking. These difficulties are often exacerbated by the strong ego-motion of the sensor platform relative to the target. Depending on the operational waveband of the sensor, environmental conditions, such as smoke, haze, fog and rain, can result in degraded target signatures, as well as partial or full occlusions. All of these factors contribute to substantial appearance variability of the target thermal signature observed by the sensor, thereby limiting the effectiveness of approaches based on, e.g., stored libraries of static a priori signatures. A few examples of MWIR signature variability from the Military Sensing Information Analysis Center (SENSIAC) ATR Algorithm Development Image Database [5] are shown in Figure 1. Moreover, one would ideally like the ATR system to be capable of generalizing on the fly, so that both unknown target types and previously unseen views of known target types can be detected, tracked and recognized, at least to within an appropriate target class.

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