Edge Detection in Images Using Gaussian Function Derivatives
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Resource Overview
This method detects image edges through Gaussian function derivatives, which is essentially equivalent to steerable wavelet transform-based edge detection across eight directional transformations.
Detailed Documentation
The approach of detecting image edges using Gaussian function derivatives is a steerable wavelet transform-based edge detection method. This technique enables edge detection along eight distinct directions in an image. In practice, this can be implemented by convolving the image with a set of directional Gaussian derivative filters, where each filter corresponds to a specific orientation (typically at 0°, 45°, 90°, 135°, and their symmetrical counterparts). Key computational steps include calculating first or second-order Gaussian derivatives, applying directional filters through 2D convolution operations, and combining the multi-directional responses using non-maximum suppression and thresholding algorithms to produce the final edge map.
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