16 September 1992 Model-based approach to real-time target detection
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Abstract
Land mine detection and extraction from infra-red (IR) scenes using real-time parallel processing is of significant interest to ground based infantry. The mine detection algorithms consist of several sub-processes to progress from raw input IR imagery to feature based mine nominations. Image enhancement is first applied; this consists of noise and sensor artifact removal. Edge grouping is used to determine the boundary of the objects. The generalized Hough Transform tuned to the land mine signature acts as a model based matched nomination filter. Once the object is found, the model is used to guide the labeling of each pixel as background, object, or object boundary. Using these labels to identify object regions, feature primitives are extracted in a high speed parallel processor. A feature based screener then compares each object's feature primitives to acceptable values and rejects all objects that do not resemble mines. This operation greatly reduces the number of objects that must be passed from a real-time parallel processor to the classifier. We will discuss details of this model- based approach, including results from actual IR field test imagery.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jay K. Hackett, Ed V. Gold, Daniel T. Long, Eugene L. Cloud, Herbert A. Duvoisin, "Model-based approach to real-time target detection", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); doi: 10.1117/12.138271; https://doi.org/10.1117/12.138271
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