Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Fast, accurate and convergent tangent estimation on digital contours
Image and Vision Computing
Comparison and improvement of tangent estimators on digital curves
Pattern Recognition
Binomial convolutions and derivatives estimation from noisy discretizations
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Estimation of the derivatives of a digital function with a convergent bounded error
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
Convergence of binomial-based derivative estimation for C2 noisy discretized curves
Theoretical Computer Science
A Modified Split-Radix FFT With Fewer Arithmetic Operations
IEEE Transactions on Signal Processing
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Differentials estimation of discrete signals is almost mandatory in digital segmentation. In our previous work, we introduced the fast level-wise convolution (LWC) and its complexity of O(2n.log2(m)). We present convergence proofs of two LWC compatible kernel families. The first one is the pseudo-binomial family, and the second one the pseudo-Gaussian family. In the experimental part, we compare our method to the Digital Straight Segment tangent estimator. Tests are done on different digitized objects at different discretization step using the DGtal library.