Spatial modeling of occlusion patterns applied to the detection of surface-laid mines

Magnus P. Lundberg; Christopher L. Brown; Magnus S.G. Uppsall

doi:10.1117/12.541558

21 September 2004 Spatial modeling of occlusion patterns applied to the detection of surface-laid mines

Magnus P. Lundberg, Christopher L. Brown, Magnus S.G. Uppsall

Proceedings Volume 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX; (2004) https://doi.org/10.1117/12.541558
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Images recorded in ground areas potentially containing surface laid land mines are considered. The first hypothesis is that the image is of clutter (grass) only, while the alternative is that the image contains a partially occluded (covered) land mine in addition to the clutter. In such a scenario, the occlusion pattern is unknown and has to be treated as a nuisance parameter. In a previous paper it was shown that deterministic treatment of the unknown occlusion pattern, in companion with the applied model, renders a substantial increase in detector performance as compared to employment of the traditional additive model. However, a deterministic assumption ignores possible correlation and additional gains could be possible by taking the spatial properties into account. In order to incorporate knowledge regarding the occlusion, the spatial distribution is characterized in terms of an underlying Markov Random Field (MRF) model. A major concern with MRF models is their complexity. Therefore, in addition to this, a less computationally demanding technique to accommodate the occlusion behavior is also proposed. The main purpose of this paper is to investigate if significant gains are possible by acknowledging the spatial dependence. Evaluation on data using real occluded targets however indicates that the gain seem to be marginal.

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Magnus P. Lundberg, Christopher L. Brown, and Magnus S.G. Uppsall "Spatial modeling of occlusion patterns applied to the detection of surface-laid mines", Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); https://doi.org/10.1117/12.541558

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KEYWORDS

Land mines

Mining

Magnetorheological finishing

Digital filtering

Sensors

Image filtering

Expectation maximization algorithms

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