The distinctiveness of a curve in a parameterized neighborhood: extraction and applications

A new feature of curves pertaining to the acceptance/rejection decision in curve detection is proposed. The feature measures a curve's distinctiveness in its neighborhood, which is modeled by a one-parameter family of curves. A computational framework based on the Hough transform for extracting...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 28; no. 8; pp. 1215 - 1222
Main Author Cheng, Yu Chin
Format Journal Article
LanguageEnglish
Published Los Alamitos, CA IEEE 01.08.2006
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Acceptance
Algorithms
Application software
Applied sciences
Artificial Intelligence
Computer science; control theory; systems
Computer Society
Ellipses
Exact sciences and technology
Extraction
Extractors
feature evaluation and selection
Feature extraction
Feature representation
geometric models
Hough transform
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Intelligence
Noise
Object recognition
Pattern analysis
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Pixel
Rejection
Shape
Signal processing
Solid modeling
Testing
Computer vision
Feature selection
feature evaluation and selection
Hough transformation
Pattern recognition
Hough transform
Object recognition
Feature representation
Geometrical model
geometric models
Feature extraction
Pattern analysis
Pattern extraction
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Summary:A new feature of curves pertaining to the acceptance/rejection decision in curve detection is proposed. The feature measures a curve's distinctiveness in its neighborhood, which is modeled by a one-parameter family of curves. A computational framework based on the Hough transform for extracting the distinctiveness feature is elaborated and examples of feature extractors for the circle and the ellipse are given. It is shown that the proposed feature can be extracted efficiently and is effective in separating signals from false positives. Experimental results with circle and ellipse testing that strongly support the efficiency and effectiveness claims are obtained. The results further demonstrate that the proposed feature exhibits good noise resiliency
Bibliography:ObjectType-Article-2
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ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2006.174