We describe an experimental, model-based automatic target recognition (ATR) system, called XTRS, for recognizing tactical vehicles in real or synthetic laser-radar (LADAR) range and intensity images corresponding to a forwardlooking, CO/sub 2/ laser radar (LADAR) that is carried either on a ground vehicle or on an airborne platform. Various aspects of the system's operation are illustrated through a variety of examples. Generic techniques are highlighted whenever possible. A first such technique is the use of feature-indicating interest images to focus attention on specific areas of the input imagery. A second is the use of an application-independent matching engine for matching features extracted from the imagery against an application-dependent appearance model hierarchy that represents the objects to be recognized. A third generic technique is the system's architectures and its control mechanism. Following the description of XTRS, we discuss XTRS's recognition performance on real data collected with the groundbased version of the ladar sensor. We then provide a detailed account of XTRS's performance on synthetic datasets created to rest the limits of system performance. Finally, we briefly discuss the use of XTRS in conjunction with the airborne version of the sensor. Overall, more than 1500 range and intensity image pairs were used throughout XTRS's development.

The application of adaptive (i.e., data-dependent) mathematical morphology techniques to range imagery, i.e., the use of structuring elements (SEs) that automatically adjust to the gray-scale values in a range image in order to deal with features of known physical sizes, is discussed. The technique is applicable to any type of image for which the distance to a scene element is available for each pixel.

A new approach to shape matching and knowledge-based signal processing, called functional template correlation (FTC), is presented in the context of 3D object recognition. Incorporating aspects of fuzzy set theory, functional templates (FTs) define, for each template point, arbitrarily complex tolerances of input image values. With this approach, object- and sensor-dependent knowledge is easily encoded in FTs, making it possible to effectively deal with uncertain or variable object appearance noise occlusion, and articulation. The output of FTC is a map of match scores, reflecting pixel-by-pixel belief of whether or not an encoded shape is present. Such maps can be combined using simple rules of arithmetic and are used to guide selective attention. The use of FTC is illustrated with examples from work in automatic recognition.>

A model-based technique is presented for recognizing 3-D objects using a novel object representation and a novel generic matching engine. Each object to be recognized is defined by an appearance model (AM) describing the expected appearances of the object over a range of aspects in a specific type of imagery. The AMs for all the objects to be recognized are organized in an AM hierarchy (AMH) defining object classes. This AMH, together with an image event extracted from the input imagery and primitives obtained by decomposing the event, constitute the input to the matching engine. This engine determines which object in the AMH most likely corresponds to the event and its primitives. The approach described is at the heart of an automatic target recognition system built by the authors.>