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Related Experiment Video

Updated: May 21, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Performance comparisons of contour-based corner detectors.

Mohammad Awrangjeb¹, Guojun Lu, Clive S Fraser

¹Department of Infrastructure Engineering, Cooperative Research Centre for Spatial Information, University of Melbourne, Melbourne 3010, Australia. mawr@unimelb.edu.au

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 31, 2012

Summary

This study compares recent contour-based corner detectors for computer vision. Indirect curvature estimation methods offer more robustness, while direct methods provide faster performance in corner detection.

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Last Updated: May 21, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Computer Vision
Image Analysis
Pattern Recognition

Background:

Corner detection is crucial for computer vision tasks like image identification.
Existing contour-based corner detectors rely on estimating curvature extrema.
A comparative analysis of recent corner detection algorithms is lacking.

Purpose of the Study:

To present a general framework for contour-based corner detection.
To discuss key factors influencing performance: curve smoothing and curvature estimation.
To compare the performance of recently proposed corner detectors.

Main Methods:

A general framework for contour-based corner detection is outlined.
Curve smoothing and curvature estimation techniques are analyzed.
Promising detectors are evaluated on large datasets using automatic and manual systems.

Main Results:

Detectors employing indirect curvature estimation exhibit greater robustness.
Detectors utilizing direct curvature estimation demonstrate faster processing speeds.
Performance is significantly impacted by curve smoothing and curvature estimation choices.

Conclusions:

The choice between direct and indirect curvature estimation involves a trade-off between speed and robustness.
This comparative study provides valuable insights for selecting appropriate corner detectors.
Further research can build upon these findings for improved corner detection algorithms.