The authors present the design of a vision algorithm compiler module for object localization that is used to construct efficient vision programs for a subtask of object localization called aspect classification. Intuitively, an aspect is a representative appearance, and can be associated with a range of viewing positions. In object localization, an object is first classified into an aspect in order to obtain a rough estimate of an object's configuration; this is followed by a numerical minimization procedure to locate the object precisely. The compiler module generates an optimal strategy for aspect classification, in the sense that the average cost of classification is minimal. The performance of the module is illustrated with several examples.>

Geometric camera calibration is the process of determining a mapping between points in world coordinates and the corresponding image locations of the points. In previous methods, calibration typically involved the iterative solution to a system of nonlinear equations. A method is presented for performing camera calibration that provides a complete, accurate solution, using only linear systems of equations. By using two calibration planes, a line-of-sight vector is defined for each pixel in the image. The effective focal point of a camera can be obtained by solving the system that defines the intersection point of the line-of-sight vectors. Once the focal point has been determined, a complete camera model can be obtained with a straightforward least-squares procedure. This method of geometric camera calibration has the advantages of being accurate, efficient, and practical for a wide variety of applications.>

A description is given of VITS (for vision task sequencer), the vision system for the autonomous land vehicle (ALV) Alvin, addressing in particular the task of road-following. The ALV vision system builds symbolic descriptions of road and obstacle boundaries using both video and range sensors. The authors discuss various road segmentation methods for video-based road-following, along with approaches to boundary extraction and transformation of boundaries in the image plane into a vehicle-centered three-dimensional scene model.>

We describe the vision system for Alvin, the Autonomous Land Vehicle, addressing in particular the task of road-following. The system builds symbolic descriptions of the road and obstacle boundaries using both video and range sensors. Road segmentation methods are described for video-based road-following, along with approaches to boundary extraction and the transformation of boundaries in the image plane into a vehicle-centered three dimensional scene model. The ALV has performed public road-following demonstrations, traveling distances up to 4.5 km at speeds up to 20 km/hr along a paved road, equipped with an RGB video camera with pan/tilt control and a laser range scanner.