Tactical imagery and geospatial data support options for automatic target acquisition

Broderick, Nicola K.; Podlesak, Michael; Dare, Paul; Jones, Simon

doi:10.1117/12.776491

Cited by 1 publication

(2 citation statements)

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“…Although image segmentation and classification parts are offered with single solutions, all alternatives for image enhancement mentioned in section 2 are measured according to their time consumption, and listed in Table 2. Consequently, inspecting the overall computation time of the ATR algorithm, which is presented in On the other hand, success of the proposed ATR algorithm is evaluated over correct classification rate and false alarm rate [2]. The probability of correct classification is calculated based on a total of 30 images.…”

Section: Operating Systemmentioning

confidence: 99%

“…In addition, in actual implementation, the disadvantage of the algorithm, caused by low correct classification probability, would be compensated with its high-speed over image sequences, which increases the importance of the false alarm rate parameter and threshold choice. On the other hand, success of the proposed ATR algorithm is evaluated over correct classification rate and false alarm rate [2]. The probability of correct classification is calculated based on a total of 30 images.…”

Section: Operating Systemmentioning

confidence: 99%

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High speed automatic target recognition based on anisotropic diffusion and discrete cosine transform

Korkusuz

2009

Automatic Target Recognition XIX

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Automatic Target Recognition is one of the most challenging and important requirements in the 21st century battlefield. Developing an algorithm which is complex enough to recognize targets and simple enough to run in real time is a challenging problem. Recognizing different targets with different size, orientation and illumination variations increase the complexity of the problem. This paper proposes a recognition approach, which tries to recognize targets fast and correctly providing robust performance. The proposed algorithm is based on anisotropic diffusion and edge detection for image segmentation and discrete cosine transform (DCT) for image classification. First, difference between target and background is increased by using the anisotropic diffusion filter. In this method, diffusion continues over low contrast pixels, decreasing the difference between smooth regions. On the other hand, diffusion stops over high contrast pixels such that the sharper boundaries are preserved. Anisotropic diffusion method controls the directions of diffusion by an error function which separates low-contrast and high-contrast neighbor pixels. Instead of using partial differential equations or robust statistical equations as an error function, a simple threshold is used to decrease iteration number and operation time. Secondly, possible targets are segmented by using "Canny's edge detection" algorithm and "connected component labeling" algorithm. Finally, possible targets and target database dimensions are reduced and compared by DCT algorithm. In order to minimize the effect of illumination variations, low frequency coefficients aren't used in this comparative study. The proposed algorithm is then tested using example pictures, and is able to find targets in less than a second.

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Section: Operating Systemmentioning

confidence: 99%

Section: Operating Systemmentioning

confidence: 99%