A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Edge Detector Design I: Parameter Selection and Noise Effects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design principles for a front-end visual system
Neural Computers
Authenticating Edges Produced by Zero-Crossing Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
An algorithm for generating a local description of the intensity transitions in an image
An algorithm for generating a local description of the intensity transitions in an image
Extensions of Scale-Space Filtering to Machine-Sensing Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A full description of image edges requires a complete characterization of their local intensity transitions, the spatial structure of those transitions, and a description of adjacent image regions. The authors propose, as a step toward this end, a 1-D algorithm for describing local intensity transitions by their Gaussian derivatives at a resolution where the support of the Gaussian smoothing matches their widths (blur). The algorithm estimates the transition width from the second derivative of 1-D Gaussian response zero-crossing slope and leads to a characterization of the transition with its first three derivatives at the resolution matching the width. The authors describe how this algorithm can be applied to images and give an example.