Automatic target detection using binary template matching

Jun, Dongsan; Sun, Sun-Gu; Park, HyunWook

doi:10.1117/1.1869997

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…that exist even when the pose, viewpoint, or lighting conditions vary and then use the features to locate targets. In template matching methods, 6 several standard patterns of a target are stored to describe the target. The correlation between an input image and the stored patterns is computed for detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic target recognition performance losses in the presence of atmospheric and camera effects

Chen

Schmid

2010

J. Electron. Imaging

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The importance of networked automatic target recognition systems for surveillance applications is continuously increasing. Because of the requirement of a low cost and limited payload, these networks are traditionally equipped with lightweight, low-cost sensors such as electro-optical (EO) or infrared sensors. The quality of imagery acquired by these sensors critically depends on the environmental conditions, type and characteristics of sensors, and absence of occluding or concealing objects. In the past, a large number of efficient detection, tracking, and recognition algorithms have been designed to operate on imagery of good quality. However, detection and recognition limits under nonideal environmental and/or sensor-based distortions have not been carefully evaluated. We introduce a fully automatic target recognition system that involves a Haar-based detector to select potential regions of interest within images, performs adjustment of detected regions, segments potential targets using a region-based approach, identifies targets using Bessel K form-based encoding, and performs clutter rejection. We investigate the effects of environmental and camera conditions on target detection and recognition performance. Two databases are involved. One is a simulated database generated using a 3-D tool. The other database is formed by imaging 10 die-cast models of military vehicles from different elevation and orientation angles. The database contains imagery acquired both indoors and outdoors. The indoors data set is composed of clear and distorted images. The distortions include defocus blur, sided illumination, low contrast, shadows, and occlusions. All images in this database, however, have a uniform (blue) background. The indoors database is applied to evaluate the degradations of recognition performance due to camera and illumination effects. The database collected outdoors includes a real background and is much more complex to process. The numerical results demonstrate that the complexity of the background and the presence of occlusions present a serious challenge for automatic target detection and recognition.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic target recognition performance losses in the presence of atmospheric and camera effects

Chen

Schmid

2010

J. Electron. Imaging

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“…The proposed IR band conversion method may simplify the construction of IR system that consist of various IR band cameras [31,32] such as a forward-looking infrared (FLIR) system for obtaining LWIR and MWIR image used for target tracking and detection. This method can be applied to the aforementioned small target detection field [25,26,27,28,29,30] by using temperature, radiation, and gray-level information obtained from the plurality of IR band images converted from arbitrary IR band images and can be utilized for accurate classification of military targets such as tanks, planes, and warships [33,34,35]. In addition, in IR and the visible image synthesis field [36] for object detection, the detection performance can be further improved by combining converted IR band images.…”

Section: Introductionmentioning

confidence: 99%

IR-Band Conversion of Target and Background Using Surface Temperature Estimation and Error Compensation for Military IR Sensor Simulation

Bae

Kim²,

Ahn

2019

Sensors

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Military infrared (IR) imaging systems utilize one or more IR wavelength-bands, among short wavelength IR (SWIR), middle wavelength IR (MWIR), and long wavelength IR (LWIR) band. The IR image wavelength-band conversion which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image is needed for IR signature modeling and image synthesis in the IR systems. However, the IR wavelength-band conversion is very challenging because absorptivity and transmittance of objects and background (atmosphere) are different according to the IR wavelength band and because radiation and reflectance characteristics of the SWIR are very different from the LWIR and MWIR. Therefore, the IR wavelength-band conversion in this paper applies to only IR targets and monotonous backgrounds at a long distance for military purposes. This paper proposes an IR wavelength-band conversion method which transforms one arbitrary IR wavelength-band image to another IR wavelength-band image by using the surface temperature estimation of an object and the error attenuation method for the estimated temperature. The surface temperature of the object is estimated by an approximated Planck’s radiation equation and the error of estimated temperature is corrected by using the slope information of exact radiance along with the approximated one. The corrected surface temperature is used for generating another IR wavelength-band image. The verification of the proposed method is demonstrated through the simulations using actual IR images obtained by thermal equipment.

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“…This technique has proven to be a relatively low-cost [11]. Besides that it is easy to adapt in many applications, it can be potentially used to determine some mechanical properties of materials [12,13].…”

Section: Introductionmentioning

confidence: 99%

Shearography as a tool to measure creep strain in sealing elastomers

et al. 2019

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In this work, a new application of digital speckle pattern shearing interferometry (shearography) for strain measurement is proposed. This optical technique is implemented to measure strain in elastic materials. Three different sealing elastomers were tested in short-term creep tests in order to assess creep compliance, which is an important mechanical property for viscoelastic materials. The creep tests were carried out applying a constant tensile load to a specimen. An in-plane shearography setup was implemented to measure the creep strains in the polymers. Results of creep strains were compared with that obtained with a commercial equipment of digital image correlation (DIC). Although some limitations were found for shearography, it was possible to verify the adaptability of this technique for strain measurement in elastomers.

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Automatic target detection using binary template matching

Cited by 12 publications

References 12 publications

Automatic target recognition performance losses in the presence of atmospheric and camera effects

Automatic target recognition performance losses in the presence of atmospheric and camera effects

IR-Band Conversion of Target and Background Using Surface Temperature Estimation and Error Compensation for Military IR Sensor Simulation

Shearography as a tool to measure creep strain in sealing elastomers

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