Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of optimal Gaussian operators in small neighbourhoods
Image and Vision Computing
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
A Systematic Design Procedure for Scalable Near-Circular Laplacian of Gaussian Operators
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Image and Vision Computing
Processing Hexagonal Images in a Virtual Environment
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Content-adaptive feature extraction using image variance
Pattern Recognition
Gradient operators for feature extraction and characterisation in range images
Pattern Recognition Letters
Autonomous operators for direct use on irregular image data
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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We present a new approach to the computation of scalable image derivative operators, based on the finite element method, that addresses the issues of method, efficiency and scale-adaptability. The design procedure is applied to the problem of approximating scalable differential operators within the framework of Schwartz distributions. Within this framework, the finite element approach allows us to define a device space in which scalable image derivative operators are implemented using a combination of piecewise-polynomial and Gaussian basis functions.Here we illustrate the approach in relation to the problem of scale-space edge detection, in which significant scale-space edge points are identified by maxima of existing edge-strength measures that are based on combinations of scale-normalised derivatives. We partition the image in order to locally identify approximate ranges of scales within which significant edge points may exist, thereby avoiding unnecessary computation of edge-strength measures across the entire range of scales.