1 April 1991 Fractal image compression and texture analysis
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Proceedings Volume 1406, Image Understanding in the '90s: Building Systems that Work; (1991); doi: 10.1117/12.47964
Event: Applied Imaging Pattern Recognition, 1990, McLean, VA, United States
Abstract
This paper describes a new method for building object models for the purpose of overlapped object recognition. The method relies on local fragments of the boundary to derive a set of autoregressive parameters that serve to detect similar boundary fragments. First a rule based algorithm which detects the occlusion of two or more objects is introduced. This algorithm makes use of aheuristic rule which take into account the number of intersection points of the boundary with a standard invariant shape and of global features (area, perimeter) to confirm the presence of occlusion. The object is then decomposed into visible parts by using first a polygonal approximation method and then the concave vertices obtained at the latter step. The decomposition algorithm prepares the input data for the description of the model and the object through the autoregressive filter method.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Baback Moghaddam, Kenneth J. Hintz, Clayton V. Stewart, "Fractal image compression and texture analysis", Proc. SPIE 1406, Image Understanding in the '90s: Building Systems that Work, (1 April 1991); doi: 10.1117/12.47964; https://doi.org/10.1117/12.47964
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KEYWORDS
Fractal analysis

Image compression

Autoregressive models

Data modeling

Detection and tracking algorithms

Object recognition

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