Computational themes in applications of visual perception

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76646/1/AIAA-1987-1674-988.pd

Model-based target recognition using laser radar imagery

Autonomous target recognition can be assisted by using CO2 laser radar data. Of these data, range data provide 3-D geometric information and Doppler data boundaries of moving targets. A powerful 3-D feature extraction algorithm based on the Hough transform is used to obtain the orientations and dimensions of the target. This information is then utilized by an inference procedure that recognizes targets based on the available evidence from the sensory data. The experimental results using actual laser radar imagery are successful and the procedure can be used for the future development of a model-based system for target recognition

Semantic Interpretation of 3D Point Clouds of Historical Objects

This paper presents the main concepts of a project under development concerning the analysis process of a scene containing a large number of objects, represented as unstructured point clouds. To achieve what we called the "optimal scene interpretation" (the shortest scene description satisfying the MDL principle) we follow an approach for managing 3-D objects based on a semantic framework based on ontologies for adding and sharing conceptual knowledge about spatial objects

Model-based target recognition using laser radar imagery

Autonomous target recognition can be assisted by using CO2 laser radar data. Of these data, range data provide 3-D geometric information and Doppler data boundaries of moving targets. A powerful 3-D feature extraction algorithm based on the Hough transform is used to obtain the orientations and dimensions of the target. This information is then utilized by an inference procedure that recognizes targets based on the available evidence from the sensory data. The experimental results using actual laser radar imagery are successful and the procedure can be used for the future development of a model-based system for target recognition

Robust Signal Restoration and Local Estimation of Image Structure

A class of nonlinear regression filters based on robust theory is introduced. The goal of the filtering is to restore the shape and preserve the details of the original noise-free signal, while effectively attenuating both impulsive and nonimpulsive noise. The proposed filters are based on robust Least Trimmed Squares estimation, where very deviating samples do not contribute to the final output. Furthermore, if there is more than one statistical population present in the processing window the filter is very likely to select adaptively the samples that represent the majority and uses them for computing the output. We apply the regression filters on geometric signal shapes which can be found, for example, in range images. The proposed methods are also useful for extracting the trend of the signal without losing important amplitude information. We show experimental results on restoration of the original signal shape using real and synthetic data and both impulsive and nonimpulsive noise. In addition, we apply the robust approach for describing local image structure. We use the method for estimating spatial properties of the image in a local neighborhood. Such properties can be used for example, as a uniformity predicate in the segmentation phase of an image understanding task. The emphasis is on producing reliable results even if the assumptions on noise, data and model are not completely valid. The experimental results provide information about the validity of those assumptions. Image description results are shown using synthetic and real data, various signal shapes and impulsive and nonimpulsive noise

Rapid Prototyping Using Three-Dimensional Computer Vision

A method for building model data for CAD and CAM purposes from physical instances using three-dimensional sensor data is presented. These techniques are suitable for Reverse Engineering of industrial parts, and can be used as a design aid as well. The nature of the reverse engineering task is quantitative, and the emphasis is on accurate recovery of the geometry of the part, whereas the object recognition task is qualitative, and aims to recognize similar shapes. The proposed method employs multiple representation to build a CAD model for the part, and to produce useful information for part analysis and process planning. The model building strategy is selected based on the obtained surface and volumetric data descriptions and their quality. A novel, robust non-linear filtering method is presented to attenuate noise from sensor data. Volumetric description is obtained by recovering a superquadric model for the whole data set. A surface characterization process is used to determine the complexity of the underlying surface. A substantial data compression can be obtained by approximating huge amount sensor data by B-spline surfaces. As a result a Boundary Representation model for Alpha-1 solid modeling system is constructed. The model data is represented both in Alpha-1 modeling language and IGES product data exchange format. Experimental results for standard geometric shapes and for sculptured free-form surfaces are presented using both real and synthetic range data