The problem of finding the stored template that is closest to a given input pattern is a typical problem in vector quantization (VQ) encoding and nearest neighbor (NN) pattern classification. This paper presents a new Triangle Inequality Nearest Neighbor Search (TINNS) algorithm that significantly reduces the number of distance calculations. This algorithm is appropriate in applications for which the computational cost of making a distance calculation is relatively expensive. Automatic Target Recognition (ATR) is one such application. This new algorithm achieves improved performance by guiding the order in which templates are tested, and using inequality constraints to prune the search space. We compare TINNS with another competing approach, as well as exhaustive search, and show that there is an appropriate application domain for each algorithm. Results are given for three applications, VQ for image compression, NN search over random templates, and target recognition in synthetic aperture radar image data.
This paper describes an approach to simultaneous estimation of target category and pose from SAR imagery using a database of target model distance transforms organized in a hierarchical tree structure. Distance transforms are shown to provide a convenient method for distortion-based model matching without requiring specific feature associations. The technique provides an approach for categorizing targets under adverse conditions including partial obscuration and interference. We show that construction of a target hierarchy using clustering techniques can lead to a tree searching strategy that prunes the tree during the search and is guaranteed to locate the best-matching target models. We also provide empirical results using synthetic target model images produced by Xpatch and show how performance is affected by signature contamination.
Cartographic feature extraction is a manpower intensive process, requiring detailed and tedious labor by a skilled cartographer to identify and delineate significant cartographic features from an image. The availability of digital image data has made feasible the usage of computers to aid in the extraction of features. In particular, much interest has been generated in the potential for AT and IU techniques to automate feature extraction. In this paper we report on techniques to assist the cartographer. In particular, the cartographer initiates by picking a point or points on the feature, and the tools complete the delineation process. We discuss two such tools, one which delineates line features and one which delineates area features. Both tools utilize neural nets to carry out the critical decisions on tracking feature boundaries. In both tools the cartographer is allowed to concentrate on the most important and professionally rewarding task, feature detection and identification, and is spared the most tedious task, feature boundary delineation.
Cartographic compilation requires precision mensuration. The calibration of mensuration processes is based on specific fiducials. External fiducials, around the exterior frame of the image, must be precisely measured to establish the overall physical geometry. Internal fiducials are provided within the image by placement of cloth panels on the ground at locations whose position is precisely known. Both types of fiducials must be known within the pixel space of a digitized image in order for the feature extraction process to be accurate with respect to delineated features. Precise mensuration of these fiducials requires that a cartographer view the image on a display and use pointing devices, such as a mouse, to pick the exact point. For accurate fiducial location, the required manual operations can be an added time- consuming task in the feature extraction process. The authors developed interactive tools which eliminate the precise pointing action for the operator. The operator is required only to 'box-in' the fiducial, using a simple drawing tool, and select either the internal or external fiducial functions; the software of the tool returns the precise location of the fiducial. The theory of the analysis used by the tool is discussed.
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