Projective Geometry and Photometry for Object Detection and Delineation.
Abstract
Computer vision systems have traditionally performed most effectively in constrained situations, where limitations on object shape or scene structure permit reliable image analysis. For example, in model-based recognition systems, the existence of 3D models of objects of interest allows the application of geometric constraints to limit the search for interpretations of low-level image information. Many problem domains, however, do not have explicit constraints on object shape or scene content. In aerial image analysis, man-made structures take on a wide variety of shapes and sizes. Existing techniques for these domains obtain only partial solutions by the use of simplifying assumptions about imaging geometry, illumination conditions, and object shape. Few of these techniques attempt to model perspective or photometric effects, which can be powerful constraints for object detection and delineation. The central hypothesis of this work, that rigorous modeling of the image acquisition process leads to improved detection and delineation of basic volumetric forms for object recognition, leads to the formulation of a set of principles for object detection and delineation. In accordance with these principles, a fully automated monocular image analysis system, PIVOT, was developed for the task domain of cartographic building extraction from aerial imagery, using original techniques for vanishing point detection, intermediate feature generation, hypothesis generation, and building model verification. A quantitative comparative evaluation methodology for object detection and delineation is presented in this work, using unbiased image space and object space performance metrics on large datasets of imagery.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 29, 1996
- Accession Number
- ADA328738
Entities
People
- Jefferey A. Shufelt
Organizations
- Carnegie Mellon University