Search Results for author:
Found 21 papers, 6 papers with code
Riesz feature representation: scale equivariant scattering network for classification tasks
In this work, we define an alternative feature representation based on the Riesz transform.
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a. k. a.
Moving Frame Net: SE(3)-Equivariant Network for Volumes
Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer vision tasks, as they apply to datasets presenting symmetries (e. g. scalings, rotations, translations).
Scale Equivariant U-Net
Therefore, this paper introduces the Scale Equivariant U-Net (SEU-Net), a U-Net that is made approximately equivariant to a semigroup of scales and translations through careful application of subsampling and upsampling layers and the use of aforementioned scale-equivariant layers.
Morphological adjunctions represented by matrices in max-plus algebra for signal and image processing
In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector.
Fully trainable Gaussian derivative convolutional layer
The Gaussian kernel and its derivatives have already been employed for Convolutional Neural Networks in several previous works.
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
We emphasize the relevance of OODD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems.
Learning Deep Morphological Networks with Neural Architecture Search
Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space.
From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets
Responding to the challenge of detecting unusual radar targets in a well identified environment, innovative anomaly and novelty detection methods keep emerging in the literature.
Deep Random Projection Outlyingness for Unsupervised Anomaly Detection
The performance of the proposed neural network approach is comparable to a state-of-the-art anomaly detection method.
Some open questions on morphological operators and representations in the deep learning era
Indeed, I firmly believe that the convergence between mathematical morphology and the computation methods which gravitate around deep learning (fully connected networks, convolutional neural networks, residual neural networks, recurrent neural networks, etc.)
Scale Equivariant Neural Networks with Morphological Scale-Spaces
The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant.
Going beyond p-convolutions to learn grayscale morphological operators
Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately.
Morphological segmentation of hyperspectral images
The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i. e., with an important number of channels.
Multivariate mathematical morphology for DCE-MRI image analysis in angiogenesis studies
A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets.
Part-based approximations for morphological operators using asymmetric auto-encoders
This paper addresses the issue of building a part-based representation of a dataset of images.
Max-plus Operators Applied to Filter Selection and Model Pruning in Neural Networks
Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks.
Regression and Classification by Zonal Kriging
Consider a family $Z=\{\boldsymbol{x_{i}}, y_{i}$,$1\leq i\leq N\}$ of $N$ pairs of vectors $\boldsymbol{x_{i}} \in \mathbb{R}^d$ and scalars $y_{i}$ that we aim to predict for a new sample vector $\mathbf{x}_0$.
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings
We propose a novel approach for pixel classification in hyperspectral images, leveraging on both the spatial and spectral information in the data.
A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images
Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification.
On distances, paths and connections for hyperspectral image segmentation
Then a finer segmentation is obtained by computing $\eta$-bounded regions and $\mu$-geodesic balls inside the $\lambda$-flat zones.
