A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Ridges for Image Analysis
Integrated Edge and Junction Detection with the Boundary Tensor
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Design of steerable filters for feature detection using canny-like criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-resolution Texture Classification Based on Local Image Orientation
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Anisotropic Smoothing Using Double Orientations
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Analysis of multiple orientations
IEEE Transactions on Image Processing
A variational approach for multi-valued velocity field estimation in transparent sequences
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences
Journal of Mathematical Imaging and Vision
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Estimation of local orientations in multivariate signals (including optical flow estimation as special case of orientation in space-time-volumes) is an important problem in image processing and computer vision. Modelling a signal using only a single orientation is often too restrictive, since occlusions and transparency happen frequently, thus necessitating the modelling and analysis of multiple orientations. In this paper, we therefore develop a unifying mathematical model for multiple orientations: beyond describing an arbitrary number of orientations in multivariate vector-valued image data such as color image sequences, it allows the unified treatment of transparently and occludingly superimposed oriented structures. Based on this model, we derive novel estimation schemes for an arbitrary number of superimposed orientations in bivariate images as well as for double orientations in signals of arbitrary signal dimensionality. The estimated orientations themselves, but also features like the number of local orientations or the angles between multiple orientations (which are invariant under rotation) can be used for various inspection, tracking and segmentation problems. We evaluate the performance of our framework on both synthetic and real data.