Fast Adaptive Algorithms For Low-Level Scene Analysis: Applications Of Polar Exponential Grid (PEG) Representation To High-Speed, Scale-And-Rotation Invariant Target Segmentation

P. S. Schenker; K. M. Wong; E. G. Cande

doi:10.1117/12.965731

12 November 1981 Fast Adaptive Algorithms For Low-Level Scene Analysis: Applications Of Polar Exponential Grid (PEG) Representation To High-Speed, Scale-And-Rotation Invariant Target Segmentation

P. S. Schenker, K. M. Wong, E. G. Cande

Author Affiliations +

Proceedings Volume 0281, Techniques and Applications of Image Understanding; (1981) https://doi.org/10.1117/12.965731
Event: 1981 Technical Symposium East, 1981, Washington, D.C., United States

Abstract

This paper presents results of experimental studies in image understanding. Two experiments are discussed, one on image correlation and another on target boundary estimation. The experiments are demonstrative of polar exponential grid (PEG) representation, an approach to sensory data coding which the authors believe will facilitate problems in 3-D machine perception. Our discussion of the image correlation experiment is largely an exposition of the PEG-representation concept and approaches to its computer implementation. Our presentation of the boundary finding experiment introduces a new robust stochastic, parallel computation segmentation algorithm, the PEG-Parallel Hierarchical Ripple Filter (PEG-PHRF).

Citation Download Citation

P. S. Schenker, K. M. Wong, and E. G. Cande "Fast Adaptive Algorithms For Low-Level Scene Analysis: Applications Of Polar Exponential Grid (PEG) Representation To High-Speed, Scale-And-Rotation Invariant Target Segmentation", Proc. SPIE 0281, Techniques and Applications of Image Understanding, (12 November 1981); https://doi.org/10.1117/12.965731

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