Utah range database

technical reportThis document describes in detail the range database provided by the Department of Computer Science at the University of Utah. This document is intended for the person(s) involved in installing the database and those using the database. The images are in four sets: the University of Utah images, SRI images, CCSP at NCSU images and the image of Victor Hugo. The scanning system is thoroughly described for the set of images scanned at Utah. The image formats for the other sets are also described

Multisensor knowledge systems

technical reportWe describe an approach which facilitates and makes explicit the organization of the knowledge necessary to map multisensor system requirements onto an appropriate assembly of algorithms, processors, sensors, and actuators. We have previously introduced the Multisensor Kernel System and Logical Sensor Specifications as a means for high-level specification of multisensor systems. The main goals of such a characterization are: to develop a coherent treatment of multisensor information, to allow system reconfiguration for both fault tolerance and dynamic response to environmental conditions, and to permit the explicit description of control. In this paper we show how Logical Sensors can be incorporated into an object-based approach to the organization of multisensor systems. In particular, we discuss: * a multisensor knowledge base, * a sensor specification scheme, and * a multisensor simulation environment. We give an example application of the system to CAD-based 2-D vision

CAGD based 3-D visual recognition

Journal ArticleA coherent automated manufacturing system needs to include CAD/CAM, computer vision, and object manipulation. Currently, most systems which support CAD/CAM do not provide for vision or manipulation and similarly, vision and manipulation systems incorporate no explicit relation to CAD/CAM models. CAD/CAM systems have emerged which allow the designer to conceive and model an object and automatically manufacture the object to the prescribed specifications. If recognition or manipulation is to be performed, existing vision systems rely on models generated in an ad hoc manner for the vision or recognition process. Although both Vision and CAD/CAM systems rely on models of the objects involved, different modeling schemes are used in each case. A more unified system will allow vision models to be generated from the CAD database. We are implementing a framework in which objects are designed using an existing CAGD system and recognition strategies based on these design models are used for visual recognition and manipulation. An example of its application is given

CAGD-based computer vision

Journal ArticleThree-dimensional model-based computer vision uses geometric models of objects and sensed data to recognize objects in a scene. Likewise, Computer Aided Geometric Design (CAGD) systems are used to interactively generate three-dimensional models during the design process. Despite this similarity, there has been a dichotomy between these fields. Recently, the unification of CAGD and vision systems has become the focus of research in the context of manufacturing automation. This paper explores the connection between CAGD and computer vision. A method for the automatic generation of recognition strategies based on the geometric properties of shape has been devised and implemented. This uses a novel technique developed for quantifying the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a Strategy Tree, is accomplished. Strategy Trees describe, in a systematic and robust manner, the search process used for recognition and localization of particular objects in the given scene. They consist of selected features which satisfy system constraints and Corroborating Evidence Subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses, are explored. Experiments utilizing 3-D data are presented

An approach to three-dimensional scene databases

technical reportCurrent image database research is concerned for the most part with the encoding and processing of two-dimensional images. However, the most successful approach to computer vision is based on 3-dimensional information, organized as either stacks of 2-D images (e.g., the intrinsic images of Barrow and Tennenbaum, or the 2 1/2 dimensional sketch of Marr) or as actual 3-dimensional data (e.g., the Multisensor Kernel System of Henderson). Efficient techniques for two-dimensional image processing have been well-developed over the last few decades and special purpose architectures are now available. However, the study of the organization, processing and analysis of three-dimensional scene data is only just beginning. We describe one approach to the representation of three-dimensional image data and evaluate several computer vision algorithms performed on the data. Finally, we describe how three-dimensional feature operations can be performed as relational database operations

The synthesis of visual recognition strategies

Journal ArticleA coherent automated manufacturing system needs to include CAD/CAM, computer vision, and object manipulation. Currently, most systems which support CAD/CAM do not provide for vision or manipulation and similarly, vision and manipulation systems incorporate no explicit relation to CAD/ CAM models. CAD/CAM systems have emerged which allow the designer to conceive and model an object and automatically manufacture the object to the prescribed specifications. If recognition or manipulation is to be performed, existing vision systems rely on models generated in an ad hoc manner for the vision or recognition process. Although both Vision and CAD/CAM systems rely on models of the objects involved, different modeling schemes are used in each case. A more unified system will allow vision models to be generated from the CAD database. The model generation should be guided by the class of object being constructed, the constraints of the vision algorithms used and the constraints imposed by the robotic workcell environment (fixtures, sensors, manipulators and effectors). We are implementing a framework in which objects are designed using an existing CAGD system and recognition strategies (logical sensor specifications) are automatically synthesized and used for visual recognition and manipulation