Between 1994 and 1997, researchers at Southwest Research Institute (SwRI) investigated methods for distributing parallel computation and data visualization under the support of an internally funded Research Initiative Program entitled the Advanced Visualization Technology Project (AVTP). A hierarchical data cache architecture was developed to provide a flexible interface between the modeling or simulation computational processes and data visualization programs. Compared to conventional post facto data visualization approaches, this data cache structure provides many advantages including simultaneous data access by multiple visualization clients, comparison of experimental and simulated data, and visual analysis of computer simulation as computation proceeds. However, since the data cache was resident on a single workstation, this approach did not address the issue of scalability of methods for avoiding the data storage bottleneck by distributing the data across multiple networked workstations. Scalability through distributed database approaches is being investigated as part of the Applied Visualization using Advanced Network Technology Infrastructure (AVANTI) project. This paper describes a methodology currently under development that is intended to avoid bottlenecks that typically arise as the result of data consumers (e.g. visualization applications) that must access and process large amounts of data that has been generated and resides on other hosts, and which must pass through a central data cache prior to being used by the data consumer.

This paper introduces a noninvasive semi-autonomous computer imaging based system for quantifying ventricular blood pool volume in embryonic chick models. The technique proceeds by first obtaining two reference images from a recorded sequence of the functioning chick heart. These reference images, which are representative of the maximum end diastolic and the minimum end systolic phases for a set of images, are used as the basis for automating subsequent operations. A noise reduction filter, an automatic thresholding algorithm, and region growing are applied to selected end phase images in order to determine the extent of the blood pool in the ventricle. The area of the isolated ventricular blood pool in each of the resulting binary images is computed and used to calculate the volume. A statistical analysis of the data obtained by this system indicates substantial improvement over previous methods.>

This paper documents the development and first offering of an interdisclipinary undergraduate course inDigital image Processing at the University of Wyoming.The course itself was designed to serve majors from a wide range of academic disclipines, although in its initial offering, it was attended mainly by students majoring in Computer Science and Electrical Engineering.

The perspective view of three noncollinear points whose image-to-object correspondence is known is studied. Such measurements are known to be ambiguous, resulting in as many as four possible solutions to the perspective three-point problem. Although there can be four solutions, it is quite often the case that there are triangle configurations that cause one, two, three, or four solutions. The results also provide a justification for the common wisdom that there are usually two solutions.>

A model-based image understanding and spatial reasoning system (SRS) has been integrated with a robot planner to provide a testbed for autonomously sensing and responding to complex but well-structured environments. The entire system operates in such a way that the robot planner requests state information for entities on a task panel from the SRS. State information may consist of the location or orientation of a latch or door on the panel or it may reflect a particular function, such as whether a latch is open or closed. Examples which illustrate the operation of the entire system with actual robotic and vision hardware are provided. The advantages and disadvantages of active/passive markers and teleoperation within the context of the integrated system are also discussed.>

No abstract available.

A method for hypothesizing the locations of features that may have been undetected by low level three-dimensional image processing algorithms is presented. The procedure relies on a predicate logic based approach to object recognition in which viewpoint dependent (observed) data is transformed into a set of viewpoint independent assertions. The detected features in these viewpoint independent assertions are variabilized and the assertions are combined to form a clause which is then tested against known models for consistency. If the features in a known model are able to instantiate the free variables in the variabilized clause, then a subgraph isomorphism is established between the features in the observed data and those of the model. Missing or undetected features in the observed data are then sought by examining the semantic relationships between model features that are instantiated and those that are not. Results of applying the method to synthetic range data are presented.

A robotic vision system is being developed which uses three-dimensional laser range data to sense its environment. The recognition subsystem incorporates topological as well as geometric information to identify viewed objects. Theorem-proving techniques are used to produce symbolic pattern matches. The major contributions of the recognition subsystem are 1) the use of viewpoint independent descriptors as the basis for representing known object models and 2) the use of theorem proving techniques to hypothesize object identities and recognize the viewed object as an instance of the appropriate viewpoint independent model descriptor. The representation scheme permits describing objects at a variety of topological and geometric levels. Furthermore, the use of viewpoint independent descriptors facilitates object recognition from a single arbitrary view despite missing information or the inclusion of viewpoint dependent artifacts. The theorem-proving approach establishes a symbolic correspondence between viewpoint independent features in the (recognized) model and features in the observed data. The recognition process uses a three-phase approach. First, hypotheses are generated which correspond to model descriptors that are likely to match the data. Evidence is applied to viable hypotheses to produce a partial match. The partial match is then used to constrain the full recognition process which leads to object identification. This strategy has been found to constrain strongly the search space of possible matches and leads to large reductions in recognition times. Results of the recognition process on synthetic and actual laser range data are presented for several objects. The system is shown to operate with robustness and alacrity.

An object recognition system has been developed which incorporates topological as well as geometric information to match viewpoint dependent object descriptors. Theorem proving techniques are used to produce symbolic pattern matches. The recognition process uses a three phase approach. First, hypotheses are generated which correspond to model descriptors that are likely to match the data. Evidence is applied to viable hypotheses to produce a partial match. The partial match is then used to constrain the full recognition process which leads to object identification. This strategy has been found to strongly constrain the search space of possible matches and leads to large reductions in recognition times. The major contributions of the system are the representation scheme and the use of theorem proving techniques to verify object identities. This approach permits describing objects at a variety of levels and facilitates recognition despite missing information or the inclusion of artifactual data. Results of the recognition process on synthetic and actual laser range data are presented for curved and planar objects. The system is shown to operate with robustness and alacrity.

With the advent of devices that can directly sense and determine the coordinates of points in space, the goal of constructing and recognizing descriptors of three-dimensional (3-D) objects is attracting the attention of many researchers in the image processing community. Unfortunately, the time required to fully sense a range image is large relative to the time required to sense an intensity image. Conversely, a single intensity image lacks the depth information required to construct 3-D object descriptors. This paper presents a method of combining the two sensory sources, intensity and range, such that the time required for range sensing is considerably reduced. The approach is to extract potential points of interest from the intensity image and then selectively sense range at these feature points. After the range information is known at these points, a graph structure representing the object in the scene is constructed. This structure is compared to the stored graph models using an algorithm for partial matching. The results of applying the method to both synthetic data and real intensity/range images are presented.

A procedure for determining the position uniquely, of a mobile robot in a three-dimensional space is presented. The method consists of viewing a single sphere with horizontal and vertical calibration great circles and computing distance and elevation and azimuth angles with respect to the sphere. The method is simple and provides good results as long as the sphere is projected onto a large portion of the image plane. Results using simulated and actual data are presented.

A method for matching three-dimensional objects against a library of models from an observed sequence of silhouettes is presented in this correspondence. Based upon the observed silhouettes, the three-dimensional structure of the object is constructed and refined. The principal moments and three primary silhouettes are computed for the constructed three-dimensional objects to represent the aggregate and detailed structure parameters. The adaptive matching technique requires that sufficient silhouettes be added to modify the structure of the unknown object until consistent and steady matching results are obtained. The library for matching is based on three primary silhouettes of the model objects. Experiments conducted show a fast convergence to a consistent result may be achieved provided that a reasonable choice of silhouettes is made.