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

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).

Towards an Machine Learning-Based Edge Computing Oriented Monitoring System for the Desert Border Surveillance Use Case

“…To this end, the authors proposed a novel thermal feature extraction algorithm, where the thermal signature was calculated using morphological operations for feeding a KNN classifier in a binary domain. As a result, the authors obtained an accuracy of 84.7% in an altitude range of 3-10 m and an accuracy of 75.2% for an altitude range of 10-20 m. • Yu et al [35] The main differences between our proposal and the works outside the desert focus for ATR systems based on IR imagery in Table 1 are as follows:…”

“…• Most of the works focused on identifying vehicles [32,33,35,[38][39][40]42], others focused on detecting the presence of animals [34] or people [37,41] by following a binary classification approach, and only one of the works considered vehicles and people in the same classification problem [36]. Our approach focuses on detecting targets within the three classes in the same system (animals, people, and vehicles) because these are the type of targets crossing the border.…”

“…Our approach focuses on detecting targets within the three classes in the same system (animals, people, and vehicles) because these are the type of targets crossing the border. • Most of the proposals were evaluated using a regular processor [32,35] or a powerful GPU [39,40,42], providing frame rates which could limit the real-time applicability of the algorithm in a low-power edge device. Other proposals did not discuss the computing platform used nor any metric for analyzing the computing cost, which limits the comparison [33,34,36,38].…”

“…Focusing on the works providing higher detection capacity metrics than ours (97%) [33,38,42], the authors reach that two of the works [38,42] were based on CNNs whose approach might not be applied in edge platforms due to its resource utilization, such as convolutional layers with 256 filters. • Regarding the computational cost, only the works in [32,35,39,40,42] provided frame rates running in a workstation. For the case of [40,42], the frame rates shown in these works are comparable with the frame rates from our proposals running on the workstation, which is relevant because they considered powerful GPUs.…”

Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

“…In recent years, the level set method (LSM) has been widely applied in the fields of image processing [ 8 ] and computer vision. This method uses the geometric metrics of the curve such as the curvature and the normal vector to control the movement of the curve, so it does not depend on the parameters of the curve and can handle the changes of the topology.…”