1 March 1991 Geometric modeling of noisy image objects
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Proceedings Volume 1468, Applications of Artificial Intelligence IX; (1991); doi: 10.1117/12.45529
Event: Orlando '91, 1991, Orlando, FL, United States
Abstract
The problem of model-based object recognition is considered as a computational process incorporating a means of clustering feature data consistent with the parts of a structural shape model. A general approach is developed that using both continuity and shape constraints for fitting axial-curve models to derived feature patterns. This integrated approach allows for noise datums to be disregarded, while missing data can be inferred by the interpretation of axial point sequences. Complete object structures are recovered using a circular operator to detect features of shape discontinuity (corners, junctions and tips). The approach is demonstrated on images from various domains, with the main result being a suburban road network analysis of a high resolution aerial image. Other results include overlapping circles, circuit board traces, and a LANDSAT image of the Mississippi river.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles A. Lipari, "Geometric modeling of noisy image objects", Proc. SPIE 1468, Applications of Artificial Intelligence IX, (1 March 1991); doi: 10.1117/12.45529; https://doi.org/10.1117/12.45529
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KEYWORDS
Data modeling

Artificial intelligence

Roads

Image processing

Visual process modeling

Pattern recognition

Visualization

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