Multiple Widths Yield Reliable Finite Differences (Computer Vision)
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of image interpolation, differentiation, and reduction using local polynomial fits
Graphical Models and Image Processing
A Discrete Expression of Canny's Criteria for Step Edge Detector Performances Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Digital Step Edges from Zero Crossing of Second Directional Derivatives
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast polynomial segmentation of digitized curves
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
Analysis and comparative evaluation of discrete tangent estimators
DGCI'05 Proceedings of the 12th international conference on Discrete Geometry for Computer Imagery
Feature controlled adaptive difference operators
Discrete Applied Mathematics
Adaptive and optimal difference operators in image processing
Pattern Recognition
Optimal difference operator selection
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Fast polynomial segmentation of digitized curves
DGCI'06 Proceedings of the 13th international conference on Discrete Geometry for Computer Imagery
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Differential operators are required to compute several characteristics for continuous surfaces, as e.g tangents, curvature, flatness, shape descriptors We propose to replace differential operators by the combined action of sets of feature detectors and locally adapted difference operators A set of simple local feature detectors is used to find the fitting function which locally yields the best approximation for the digitized image surface For each class of fitting functions, we determine which difference operator locally yields the best result in comparison to the differential operator Both the set of feature detectors and the difference operator for a function class have a rigid mathematical structure, which can be described by Groebner bases In this paper we describe how to obtain discrete approximates for the Laplacian differential operator and how these difference operators improve the performance of the Laplacian of Gaussian edge detector.