8 citations · 11 across the 4 of their papers we have counts for
5 papers · 1 filter
3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
Valentin Oreiller, Vincent Andrearczyk, Julien Fageot +2
Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can b…
Local Rotation Invariance in 3D CNNs
Vincent Andrearczyk, Julien Fageot, Valentin Oreiller +2
Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arb…
Rotational 3D Texture Classification Using Group Equivariant CNNs
Vincent Andrearczyk, Adrien Depeursinge
Convolutional Neural Networks (CNNs) traditionally encode translation equivariance via the convolution operation. Generalization to other transformations has recently received attr…
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Vincent Andrearczyk, Paul F. Whelan
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is impor…
Texture segmentation with Fully Convolutional Networks
Vincent Andrearczyk, Paul F. Whelan
In the last decade, deep learning has contributed to advances in a wide range computer vision tasks including texture analysis. This paper explores a new approach for texture segme…