Lambert E. Wixson

Automating knowledge acquisition for aerial image interpretation

The interpretation of aerial imagery requires substantial knowledge about the scene under consideration. Knowledge about the type of scene-airport, suburban housing development, urban city-aids in low-level and intermediate level image analysis and will drive high-level interpretation by constraining search for plausible consistent scene models. Collecting and representing large knowledge bases requires specialized tools. In this paper we describe the organization of a set of tools for interactive knowledge acquisition of scene primitives and spatial constraints for interpretation of aerial imagery. These tools include a user interface for interactive knowledge acquisition, an automated knowledge compiler that transforms schemabased assertions into productions that are directly executable by our interpretation system, and a performance analysis tool that generates a critique of the final interpretation. The generality of these tools is demonstrated by the generation of rules for a new task, suburban house scenes, and the analysis of a set of imagery by our interpretation system.

Image alignment for precise camera fixation and aim

Two important problems in camera control are how to keep a moving camera fixated on a target point, and how to precisely aim a camera, whose approximate pose is known, towards a given 3D position. This paper describes how electronic image alignment techniques can be used to solve these problems, as well as provide other benefits such as stabilized video. Hence, stabilized, fixated imagery is obtained despite large latencies in the control loop, even for simple control strategies. These techniques have been tested using an airborne camera and real-time affine image alignment.

Illumination assessment for vision-based traffic monitoring

Vision systems that must operate autonomously over varying environmental conditions often must use different parameter values or algorithms depending on these conditions. A key problem is how to automatically assess the incoming imagery to determine these appropriate parameters and algorithms. This paper presents methods for such assessment. Specifically, it presents measures for determining whether the scene is well-lit (i.e. whether objects entire extent is visible, versus just their lights), whether the scene has sufficient contrast, and whether objects are casting shadows. The methods are applied in the domain of traffic monitoring, are based on empirical data, and have been tested on videotape segments from over 25 different scenes.