This paper examines object pair matching within LADAR (laser radar) imagery. By manipulating the three-dimensional data contained within a LADAR range image, it is possible to create a version of the scene as seen from above. In the transformed view, a fuzzy region represents each object, and the spatial relationship between two objects is represented by a force histogram. Matching two scenes then comes down to comparing force histograms. Each comparison gives a degree of similarity between the two object pairs considered, as well as an assessment of the pose parameters. These values can finally be combined to find a correct scene matching. Experiments on synthetic data as well as on real data demonstrate the applicability of our approach.

Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, pattern recognition, and computer vision. High-level computer vision applications hold a great potential for fuzzy set theory because of its links to natural language. Linguistic scene description, a language-based interpretation of regions and their relationships, is one such application that is starting to bear the fruits of fuzzy set theoretic involvement. In this paper, we are expanding on two earlier endeavors. We introduce new families of fuzzy directional relations that rely on the computation of histograms of forces. These families preserve important relative position properties. They provide inputs to a fuzzy rule base that produces logical linguistic descriptions along with assessments as to the validity of the descriptions. Each linguistic output uses hedges from a dictionary of about 30 adverbs and other terms that can be tailored to individual users. Excellent results from several synthetic and real image examples show the applicability of this approach.