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RNA Secondary Structure Prediction Using High-throughput SHAPE
13:42

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Published on: May 31, 2013

A hierarchical syntactic shape analyzer.

T Pavlidis1, F Ali

  • 1SENIOR MEMBER, IEEE, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720; Department of Electrical Engineering and Computer Scie.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a parser for describing object shapes from contour data. The method simplifies contour analysis for improved shape recognition and description.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Traditional contour description methods often require complex grammars to ensure accuracy and noise reduction.
  • Describing images based on plane objects and their shapes is a common practice in computer vision.

Purpose of the Study:

  • To develop a parser that transforms piecewise linear contour data into high-level shape descriptions.
  • To enable both accurate contour description and efficient shape recognition.

Main Methods:

  • A parser is designed to accept piecewise linear contour encodings as input.
  • Numerical preprocessing is employed to reduce noise and extract basic shape information from contours.
  • The preprocessed data is then used with simpler grammars for detailed contour description.

Main Results:

  • The parser outputs a string of high-level descriptions, including arcs, corners, protrusions, and intrusions.
  • Numerical preprocessing effectively removes noise and answers basic shape queries.
  • Simpler grammars are sufficient for contour description due to effective preprocessing.

Conclusions:

  • The proposed method offers a more efficient approach to contour description and recognition.
  • The technique is versatile, demonstrated by examples from handwritten numerals, white blood cells, and printed wiring circuit boards.