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Your search keyword '"Shufelt, Jefferey A."' showing total 18 results
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18 results on '"Shufelt, Jefferey A."'

8. Performance evaluation and analysis of vanishing point detection techniques

Author

Shufelt, Jefferey A.

Subjects
Image processing -- Research, Object recognition (Computers) -- Research, Performance -- Evaluation, Photogrammetry -- Research, Cartography -- Information management
Abstract

Vanishing point detection algorithms based on a Gaussian sphere representation have been employed in a variety of computer vision systems, for extracting 3D line orientations as a first step towards object detection. Typically, these algorithms have been applied to imagery with strong perspective effects and with little noise or texture, resulting in good solutions for line orientations. However, these algorithms can fail if perspective effects are weak, or if texture edges are predominant; they also fail to take advantage of knowledge about the objects to be detected. In this paper, two new techniques for robust vanishing point detection on the Gaussian sphere are presented; primitive-based vanishing point analysis and interpretation plane error modeling. The performance of these methods, along with two other existing methods from the literature, are quantitatively evaluated and compared for the task of building detection in complex aerial imagery. Index Terms - Object recognition, shape representation, vanishing points, photogrammetry, building detection, cartographic feature extraction, performance evaluation.

Published
1999

11. Fusion of monocular cues to detect man-made structures in aerial imagery

Author

Shufelt, Jefferey and Mckeown, David M

Subjects
Earth Resources And Remote Sensing
Abstract

The extraction of buildings from aerial imagery is a complex problem for automated computer vision. It requires locating regions in a scene that possess properties distinguishing them as man-made objects as opposed to naturally occurring terrain features. It is reasonable to assume that no single detection method can correctly delineate or verify buildings in every scene. A cooperative-methods paradigm is useful in approaching the building extraction problem. Using this paradigm, each extraction technique provides information which can be added or assimilated into an overall interpretation of the scene. Thus, the main objective is to explore the development of computer vision system that integrates the results of various scene analysis techniques into an accurate and robust interpretation of the underlying three dimensional scene. The problem of building hypothesis fusion in aerial imagery is discussed. Building extraction techniques are briefly surveyed, including four building extraction, verification, and clustering systems. A method for fusing the symbolic data generated by these systems is described, and applied to monocular image and stereo image data sets. Evaluation methods for the fusion results are described, and the fusion results are analyzed using these methods.

Published
1991

13. Projective Geometry and Photometry for Object Detection and Delineation.

Author

CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE, Shufelt, Jefferey A., CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE, and Shufelt, Jefferey A.

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.

Published
1996

14. Performance evaluation and analysis of monocular building extraction from aerial imagery

Author

Shufelt, Jefferey A.

Subjects
Aerial photogrammetry -- Research, Machine vision -- Research, Building -- Models, Object recognition (Computers) -- Research
Abstract

Research in monocular building extraction from aerial imagery has neglected performance evaluation in three areas: unbiased metrics for quantifying detection and delineation performance, an evaluation methodology for applying these metrics to a representative body of test imagery, and an approach for understanding the impact of image and scene content on building extraction algorithms. This paper addresses these areas with an end-to-end performance evaluation of four existing monocular building extraction systems, using image space and object space-based metrics on 83 test images of 18 sites. This analysis is supplemented by an examination of the effects of image obliquity and object complexity on system performance, as well as a case study on the effects Of edge fragmentation. This widely applicable performance evaluation approach highlights the consequences of various traditional assumptions about camera geometry, image content, and scene structure, and demonstrates the utility of rigorous photogrammetric object space modeling and primitive-based representations for building extraction. Index Terms - Monocular building extraction, performance evaluation, object detection, feature delineation, photogrammetry, computer vision, metrics.

Published
1999

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