An Optimal Scale for Edge Detection

IJCAI 1987 pp. 745-748

doi:10.21236/ADA202747 /ijcai/1987/geiger1987ijcai-optimal/

Abstract

Many problems in early vision are ill posed. 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 $\lambda $ and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter $\lambda $ is derived from regularization analysis for the case of small values of $\lambda$. 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.

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Cite

Text

Geiger and Poggio. "An Optimal Scale for Edge Detection." International Joint Conference on Artificial Intelligence, 1987. doi:10.21236/ADA202747

Markdown

[Geiger and Poggio. "An Optimal Scale for Edge Detection." International Joint Conference on Artificial Intelligence, 1987.](https://mlanthology.org/ijcai/1987/geiger1987ijcai-optimal/) doi:10.21236/ADA202747

BibTeX

@inproceedings{geiger1987ijcai-optimal,
  title     = {{An Optimal Scale for Edge Detection}},
  author    = {Geiger, Davi and Poggio, Tomaso A.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1987},
  pages     = {745-748},
  doi       = {10.21236/ADA202747},
  url       = {https://mlanthology.org/ijcai/1987/geiger1987ijcai-optimal/}
}