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Title: Multiscale characterization and analysis of shapes

Abstract

An adaptive multiscale method approximates shapes with continuous or uniformly and densely sampled contours, with the purpose of sparsely and nonuniformly discretizing the boundaries of shapes at any prescribed resolution, while at the same time retaining the salient shape features at that resolution. In another aspect, a fundamental geometric filtering scheme using the Constrained Delaunay Triangulation (CDT) of polygonized shapes creates an efficient parsing of shapes into components that have semantic significance dependent only on the shapes' structure and not on their representations per se. A shape skeletonization process generalizes to sparsely discretized shapes, with the additional benefit of prunability to filter out irrelevant and morphologically insignificant features. The skeletal representation of characters of varying thickness and the elimination of insignificant and noisy spurs and branches from the skeleton greatly increases the robustness, reliability and recognition rates of character recognition algorithms.

Inventors:
 [1];  [2]
  1. (Los Alamos, NM)
  2. (Sunnyvale, CA)
Issue Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM
OSTI Identifier:
874453
Patent Number(s):
6393159
Assignee:
The Regents of the University of California (Los Alamos, CA) LANL
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
multiscale; characterization; analysis; shapes; adaptive; method; approximates; continuous; uniformly; densely; sampled; contours; purpose; sparsely; nonuniformly; discretizing; boundaries; prescribed; resolution; time; retaining; salient; shape; features; aspect; fundamental; geometric; filtering; scheme; constrained; delaunay; triangulation; cdt; polygonized; creates; efficient; parsing; components; semantic; significance; dependent; structure; representations; skeletonization; process; generalizes; discretized; additional; benefit; prunability; filter; irrelevant; morphologically; insignificant; skeletal; representation; characters; varying; thickness; elimination; noisy; spurs; branches; skeleton; greatly; increases; robustness; reliability; recognition; rates; character; algorithms; /382/345/

Citation Formats

Prasad, Lakshman, and Rao, Ramana. Multiscale characterization and analysis of shapes. United States: N. p., 2002. Web.
Prasad, Lakshman, & Rao, Ramana. Multiscale characterization and analysis of shapes. United States.
Prasad, Lakshman, and Rao, Ramana. Tue . "Multiscale characterization and analysis of shapes". United States. https://www.osti.gov/servlets/purl/874453.
@article{osti_874453,
title = {Multiscale characterization and analysis of shapes},
author = {Prasad, Lakshman and Rao, Ramana},
abstractNote = {An adaptive multiscale method approximates shapes with continuous or uniformly and densely sampled contours, with the purpose of sparsely and nonuniformly discretizing the boundaries of shapes at any prescribed resolution, while at the same time retaining the salient shape features at that resolution. In another aspect, a fundamental geometric filtering scheme using the Constrained Delaunay Triangulation (CDT) of polygonized shapes creates an efficient parsing of shapes into components that have semantic significance dependent only on the shapes' structure and not on their representations per se. A shape skeletonization process generalizes to sparsely discretized shapes, with the additional benefit of prunability to filter out irrelevant and morphologically insignificant features. The skeletal representation of characters of varying thickness and the elimination of insignificant and noisy spurs and branches from the skeleton greatly increases the robustness, reliability and recognition rates of character recognition algorithms.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2002},
month = {1}
}

Patent:

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