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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Algorithms for detecting m-dimensional objects in N-dimensional spaces.

V S Alagar1, L H Thiel

  • 1Department of Computer Science, Concordia University, Montreal, P.Q., Canada.

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

This study introduces algorithms for line detection in images with uniform noise. A generalized Duda-Hart procedure with mixed quantization efficiently finds lines, reducing spurious results.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Geometry

Background:

  • Line detection in 2D image spaces is crucial for image analysis.
  • Uniformly distributed noise presents challenges for traditional algorithms.
  • Existing methods may struggle with accuracy and efficiency in noisy environments.

Purpose of the Study:

  • To develop and evaluate exact and approximate algorithms for line detection in noisy 2D image spaces.
  • To investigate the impact of different quantization schemes on algorithm performance under varying probabilistic assumptions.
  • To analyze the time complexity and accuracy of a novel generalized Duda-Hart procedure.

Main Methods:

  • Discussion of transform methods and probability measures for parameter estimation.
  • Evaluation and comparison of various quantization schemes for transformed image spaces.
  • Implementation and analysis of a generalized Duda-Hart procedure with mixed quantization.

Main Results:

  • Different quantization schemes are optimal for different probabilistic assumptions.
  • The generalized Duda-Hart procedure with mixed quantization achieves a time complexity bounded by O(ptm(n-m)2).
  • This procedure demonstrates high accuracy in detecting true lines while minimizing spurious detections.

Conclusions:

  • The proposed algorithms and quantization schemes offer improved line detection in noisy images.
  • The generalized Duda-Hart procedure provides an efficient and accurate method for identifying m-flats in n-space.
  • This work contributes to advancements in robust image analysis techniques.