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Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks

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2 Author(s)
Andres Huertas ; Intelligent Systems Group, Department of Electrical Engineering, University of Southern California, Los Angeles, CA, 90089. ; Gerard Medioni

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].

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:PAMI-8 ,  Issue: 5 )