Paper
31 May 1996 Introduction to the detection of target field structure with data fusion of density and chord information
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Abstract
The detection of spatial structure in fields of small targets is a challenging problem in high- level vision that hitherto has not been solved effectively. Although regular arrays of targets can be detected using local density and nearest-neighbor position estimators, such methods generally do not describe target field structure at multiple resolution levels. In this paper, we present several multiresolution techniques for detecting regular spatial structure in target field imagery. Our methods are tolerant of moderate deviations in target position, i.e., are amenable to irregular target placement, which is an important advantage when analyzing positions of targets deployed from slowly moving vehicles. We initially discuss local density computations based on pyramid-structured image representations, then progress to chord-based determination of target field structure. Methods for computing chord distribution parameters from histograms are presented that can be used in model-based detection of spatial structure in various target array configurations. Additionally, we discuss work-in-progress pertaining to data fusion methodologies for determining target field placement and structure in a variety of field scenarios. Analyses emphasize computational cost and error, as well as space requirements for parallel processing applications.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark S. Schmalz and Gerhard X. Ritter "Introduction to the detection of target field structure with data fusion of density and chord information", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); https://doi.org/10.1117/12.241243
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Cited by 1 scholarly publication.
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KEYWORDS
Target detection

Detection and tracking algorithms

Data fusion

Image fusion

Fourier transforms

Image processing

Sensors

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