Length estimators for digitized contours
Computer Vision, Graphics, and Image Processing
Discrete multidimensional Jordan surfaces
CVGIP: Graphical Models and Image Processing
Fast computation of the normal vector field of the surface of a 3-D discrete object
DCGA '96 Proceedings of the 6th International Workshop on Discrete Geometry for Computer Imagery
A Comparative Evaluation of Length Estimators of Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast, accurate and convergent tangent estimation on digital contours
Image and Vision Computing
Surface Shading in the Cuberille Environment
IEEE Computer Graphics and Applications
Multigrid convergence and surface area estimation
Proceedings of the 11th international conference on Theoretical foundations of computer vision
Binomial convolutions and derivatives estimation from noisy discretizations
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Discrete surfaces segmentation into discrete planes
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Technical Section: Normals estimation for digital surfaces based on convolutions
Computers and Graphics
Convergence of binomial-based derivative estimation for C2 noisy discretized curves
Theoretical Computer Science
Curvature estimation for discrete curves based on auto-adaptive masks of convolution
CompIMAGE'10 Proceedings of the Second international conference on Computational Modeling of Objects Represented in Images
Integral based curvature estimators in digital geometry
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Multigrid convergent curvature estimator
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
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In this paper, we present a method that we call on-surface convolution which extends the classical notion of a 2D digital filter to the case of digital surfaces (following the cuberille model). We also define an averaging mask with local support which, when applied with the iterated convolution operator, behaves like an averaging with large support. The interesting property of the latter averaging is the way the resulting weights are distributed: they tend to decrease following a "continuous" geodesic distance within the surface. We eventually use the iterated averaging followed by convolutions with differentiation masks to estimate partial derivatives and then normal vectors over a surface. We provide an heuristics based on [14] for an optimal mask size and show results.