Filtering by repeated integration
SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
What every computer scientist should know about floating-point arithmetic
ACM Computing Surveys (CSUR)
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Journal of Mathematical Imaging and Vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Continuous wavelet transform with arbitrary scales and O(N) complexity
Signal Processing
Robust Real-Time Face Detection
International Journal of Computer Vision
Design of steerable filters for feature detection using canny-like criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-squares image resizing using finite differences
IEEE Transactions on Image Processing
Fast space-variant elliptical filtering using box splines
IEEE Transactions on Image Processing
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We present an efficient algorithm to compute multidimensional spatially variant convolutions--or inner products--between N-dimensional signals and B-splines--or their derivatives--of any order and arbitrary sizes. The multidimensional B-splines are computed as tensor products of 1-D B-splines, and the input signal is expressed in a B-spline basis. The convolution is then computed by using an adequate combination of integration and scaled finite differences as to have, for moderate and large scale values, a computational complexity that does not depend on the scaling factor. To show in practice the benefit of using our spatially variant convolution approach, we present an adaptive noise filter that adjusts the kernel size to the local image characteristics and a high sensitivity local ridge detector.