An Optimal Scale for Edge Detection
Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Perception. Pages 745-747.
Abstract
Many problems in earl y vision are ill posed 1 . Edge detection is a typical example. This paper applies regularization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by .the regularization parameter A and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter A is derived from regularization analysis, showing that the scale of the filter is a function of the signal-to-noise ratio. We also discuss the method of Generalized Cross Validation for obtaining the optimal filter scale. Finally, we use our framework to explain two perceptual phenomena: coarsely quantized images becoming recognizable by either blurring or adding noise.
@inproceedings{ijcai1987p150,
title = {An Optimal Scale for Edge Detection},
author = {Geiger, Davi and Poggio, Tomaso A.},
booktitle = {Proceedings of the Tenth International Joint Conference on
Artificial Intelligence, {IJCAI-87}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John McDermott},
pages = {745--747},
year = {1987},
note = {Perception},
url = {https://ijcai.org/proceedings/1987/150},
}
TY - CPAPER TI - An Optimal Scale for Edge Detection AU - Geiger, Davi AU - Poggio, Tomaso A. T2 - Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 PB - International Joint Conferences on Artificial Intelligence PY - 1987 SP - 745 EP - 747 UR - https://ijcai.org/proceedings/1987/150 ER -Copied
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