Design of optimal Gaussian operators in small neighbourhoods
Image and Vision Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Reasoning About Edges in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Determination of Filter Scales for Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptive approach to scale selection for line and edge detection
Pattern Recognition Letters
Digital image processing
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Adaptive-Scale Filtering and Feature Detection Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Device Space Design for Efficient Scale-Space Edge Detection
ICCS '02 Proceedings of the International Conference on Computational Science-Part I
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
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The problem of scale is of fundamental interest in image processing, as the features that we visually perceive and find meaningful vary significantly depending on their size and extent. It is well known that the strength of a feature in an image may depend on the scale at which the appropriate detection operator is applied. It is also the case that many features in images exist significantly over a limited range of scales, and, of particular interest here, that the most salient scale may vary spatially over the feature. Hence, when designing feature detection operators, it is necessary to consider the requirements for both the systematic development and adaptive application of such operators over scale- and image-domains. We present a new approach to the design of scalable derivative edge detectors, based on the finite element method, that addresses the issues of method and scale adaptability. The finite element approach allows us to formulate scalable image derivative operators that can be implemented using a combination of piecewise-polynomial and Gaussian basis functions. The issue of scale is addressed by partitioning the image in order to identify local key scales at which significant edge points may exist. This is achieved by consideration of empirically designed functions of local image variance.