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Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks
May 1986 (vol. 8 no. 5)
pp. 651-664
Andres Huertas, Intelligent Systems Group, Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089.
Gerard Medioni, Intelligent Systems Group, Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089.
We present a system that takes a gray level image as input, locates edges with subpixel accuracy, and links them into lines. Edges are detected by finding zero-crossings in the convolution of the image with Laplacian-of-Gaussian (LoG) masks. The implementation differs markedly from M.I.T.'s as we decompose our masks exactly into a sum of two separable filters instead of the usual approximation by a difference of two Gaussians (DOG). Subpixel accuracy is obtained through the use of the facet model [1]. We also note that the zero-crossings obtained from the full resolution image using a space constant ¿ for the Gaussian, and those obtained from the 1/n resolution image with 1/n pixel accuracy and a space constant of ¿/n for the Gaussian, are very similar, but the processing times are very different. Finally, these edges are grouped into lines using the technique described in [2].
Citation:
Andres Huertas, Gerard Medioni, "Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 5, pp. 651-664, May 1986, doi:10.1109/TPAMI.1986.4767838
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