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Author's Reply
1Machine Vision International, Ann Arbor, MI 48104.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
Summary
The Laplacian zero-crossing operator is less effective than the second directional derivative for edge detection. Our findings show it misses and misplaces edges, especially in noisy images.
Area of Science:
- Computer Vision
- Image Processing
- Edge Detection Algorithms
Background:
- Edge detection is crucial for image analysis.
- Evaluating the effectiveness of different edge detection operators is an ongoing research area.
- Previous studies by Grimson and Hildreth suggested comparable performance for certain edge operators.
Purpose of the Study:
- To compare the effectiveness of the Laplacian zero-crossing operator against the second directional derivative edge operator.
- To demonstrate the limitations of the Laplacian operator in utilizing neighborhood information.
- To re-evaluate edge operator performance on noisy datasets.
Main Methods:
- Applied the Laplacian of Gaussian (LoG) edge operator with a Gaussian smoother (std dev 5.0) and a large neighborhood size (50x50).
- Tested the LoG operator on an aerial image of a mobile home park and a noisy checkerboard image.
- Compared the results against the second directional derivative edge operator.
Main Results:
- The LoG operator with the specified parameters missed and misplaced edges on the aerial image.
- Performance of the LoG operator on a noisy checkerboard image was inferior to the second directional derivative operator.
- Evidence suggests the LoG operator does not effectively use neighborhood information compared to the alternative.
Conclusions:
- The Laplacian zero-crossing operator is less effective than the second directional derivative for edge detection, particularly with specific parameter choices.
- The study highlights the importance of parameter selection and neighborhood information in edge operator performance.
- Further research is needed on comprehensive edge operator evaluation methodologies.
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