A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
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The extraction of image edges is a fundamental task in early computer vision. The successful edge detection depends on the selection of optimal convolution kernels that are appropriate to the local grey value changes. Unlike previous attempts that use a bank of filters, we introduce in this paper a computational method of estimating adaptive kernels from the covariance matrix of local grey value changes. Such an adaptive kernel can be deformed at any scale in an arbitrary direction. Some results on edge detection using adaptive kernels are also presented in this paper.