An information system for target recognition

Horney, Tobias; Ahlberg, Jörgen; Grönwall, Christina; Folkesson, Martin; Silvervarg, Karin; Fransson, Jorgen; Klasen, Lena M.; Jungert, Erland; Lantz, Fredrik; Ulvklo, Morgan

doi:10.1117/12.540968

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…By rotating the view from horizontal to vertical automatic methods looking for and identifying rectangles and or boxes of target size seem attractive. Work on target classification using these techniques is in progress [ 23].…”

Section: Discussionmentioning

confidence: 99%

Performance of 3D laser radar through vegetation and camouflage

Steinvall

Larsson

Gustafsson

et al. 2005

Laser Source and System Technology for Defense and Security

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One of the more exciting capabilities foreseen for future 3-D imaging laser radars is to see through vegetation and camouflage nettings. We have used ground based and airborne scanning laser radars to collect data of various types of terrain and vegetation. On some occasions reference targets were used to collect data on reflectivity and to evaluate penetration. The data contains reflectivity and range distributions and were collected at 1.5 and 1.06 µm wavelength with range accuracies in the 1-10 cm range. The seasonal variations for different types of vegetation have been studied. A preliminary evaluation of part of the data set was recently presented at another SPIE conference.Since then the data have been analyzed in more detail with emphasis on testing algorithms and future system performance by simulation of different sensors and scenarios. Evaluation methods will be discussed and some examples of data sets will be presented.

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Section: Discussionmentioning

confidence: 99%

Performance of 3D laser radar through vegetation and camouflage

Steinvall

Larsson

Gustafsson

et al. 2005

Laser Source and System Technology for Defense and Security

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“…The majority of the classification research involving thermal imagery has involved generating features based on geometric features (Fang & Wu, 2007;Prokoski, 2000;Sevigny et al, 1983;Strickland, 1994) rather than thermal-physical features generated from the target's surface (Bharadwaj & Carin, 2002;El Maadi & Maldague, 2007;Horney et al, 2004;Nandhakumar & Aggarwal, 1988). Many machine vision industrial applications rely on thermal features generated from the surface of objects to monitor quality control (Al-Habaibeh et al, 2004;Maldague, 1994;Meriaudeau, 2007), but they normally involve feature generation in a controlled indoors environment using a active thermal excitation to monitor packaging standards and detect anomalies in products.…”

Section: Figurementioning

confidence: 99%

Passive infrared thermographic imaging for mobile robot object identification

Fehlman

Hinders

2009

Journal of Field Robotics

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The usefulness of thermal infrared imaging as a mobile robot sensing modality is explored, and a set of thermalphysical features used to characterize passive thermal objects in outdoor environments is described. Objects that extend laterally beyond the thermal camera's field of view, such as brick walls, hedges, picket fences, and wood walls, as well as compact objects that are laterally within the thermal camera's field of view, such as metal poles and tree trunks, are considered. Classification of passive thermal objects is a subtle process because they are not a source for their own emission of thermal energy. A detailed analysis is included of the acquisition and preprocessing of thermal images, as well as the generation and selection of thermal-physical features from these objects within thermal images. Classification performance using these features is discussed, as a precursor to the design of a physics-based model to automatically classify these objects. Published

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“…The framework of the target recognition method proposed in this paper is described in [3,4,15]. The framework is a query-based multi sensor information system for ground target recognition.…”

Section: B the Atr Frameworkmentioning

confidence: 99%

“…We assume that the target scene is detected [3,4,15]. A scene of approximately 15 15 meters containing the target is selected manually.…”

Section: B Preprocessingmentioning

confidence: 99%

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Ground Target Recognition Using Rectangle Estimation

Grönwall

Gustafsson

Millnert

2006

IEEE Trans. on Image Process.

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We propose a ground target recognition method based on 3D laser radar data. The method handles general 3D scattered data. It is based on the fact that manmade objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identification of segments that represent the targets functional/main parts and target matching with CAD models. The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.Keywords: Rectangle estimation, laser radar, automatic target recognition 1 Ground target recognition using rectangle estimation Christina Grönwall, Fredrik Gustafsson, Mille Millnert Abstract-We propose a ground target recognition method based on 3D laser radar data. The method handles general 3D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identi cation of segments that represent the target's functional/main parts and target matching with CAD models.The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.

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An information system for target recognition

Cited by 8 publications

References 10 publications

Performance of 3D laser radar through vegetation and camouflage

Performance of 3D laser radar through vegetation and camouflage

Passive infrared thermographic imaging for mobile robot object identification

Ground Target Recognition Using Rectangle Estimation

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