110 research outputs found
Comparative study on morphological principal component analysis of hyperspectral images
International audienceThis paper deals with a problem of reducing the dimension of hyperspectral images using the principal component analysis. Since hyperspectral images are always reduced before any process, we choose to do this reduction by adding spatial information that can be useful then for classification process; to do it we choose to project our data in new spaces thanks mathematical morphology
Morphological Principal Component Analysis for Hyperspectral Image Analysis
International audienceThis paper deals with a problem of dimensionality reduction for hyperspectral images using the principal component analysis. Hyper-spectral image reduction is improved by adding structural/spatial information to the spectral information, by means of mathematical morphology tools. Then it can be useful in supervised classification for instance. The key element of the approach is the computation of a covariance matrix which integrates simultaneously both spatial and spectral information. Thanks to these new covariance matrices, new features can be extracted. To prove the efficiency of these new features we have conducted an extended study showing the interest of the structural/spatial information
Unsupervised Morphological Multiscale Segmentation of Scanning Electron Microscopy Images
This paper deals with a problem of unsupervised multiscale segmentation in the domain of scanning electron microscopy, which is tackled by mathematical morphology techniques. The proposed approach includes various steps. First, the image is decomposed into various compact scales of representation, where objects at each scale are homogeneous in size. Multiscale decomposition is based on a morphological scale-space followed by scale merging using hierarchical clustering and earth mover distance. Then the compact scales are segmented independently using watershed transform. Finally the segmented scales are combined using a tree of objects in order to obtain a multiscale segmentation
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Uncertainty quantification is critical for deploying deep neural networks
(DNNs) in real-world applications. An Auxiliary Uncertainty Estimator (AuxUE)
is one of the most effective means to estimate the uncertainty of the main task
prediction without modifying the main task model. To be considered robust, an
AuxUE must be capable of maintaining its performance and triggering higher
uncertainties while encountering Out-of-Distribution (OOD) inputs, i.e., to
provide robust aleatoric and epistemic uncertainty. However, for vision
regression tasks, current AuxUE designs are mainly adopted for aleatoric
uncertainty estimates, and AuxUE robustness has not been explored. In this
work, we propose a generalized AuxUE scheme for more robust uncertainty
quantification on regression tasks. Concretely, to achieve a more robust
aleatoric uncertainty estimation, different distribution assumptions are
considered for heteroscedastic noise, and Laplace distribution is finally
chosen to approximate the prediction error. For epistemic uncertainty, we
propose a novel solution named Discretization-Induced Dirichlet pOsterior
(DIDO), which models the Dirichlet posterior on the discretized prediction
error. Extensive experiments on age estimation, monocular depth estimation, and
super-resolution tasks show that our proposed method can provide robust
uncertainty estimates in the face of noisy inputs and that it can be scalable
to both image-level and pixel-wise tasks.Comment: 22 page
Learning Deep Morphological Networks with Neural Architecture Search
Deep Neural Networks (DNNs) are generated by sequentially performing linear
and non-linear processes. Using a combination of linear and non-linear
procedures is critical for generating a sufficiently deep feature space. The
majority of non-linear operators are derivations of activation functions or
pooling functions. Mathematical morphology is a branch of mathematics that
provides non-linear operators for a variety of image processing problems. We
investigate the utility of integrating these operations in an end-to-end deep
learning framework in this paper. DNNs are designed to acquire a realistic
representation for a particular job. Morphological operators give topological
descriptors that convey salient information about the shapes of objects
depicted in images. We propose a method based on meta-learning to incorporate
morphological operators into DNNs. The learned architecture demonstrates how
our novel morphological operations significantly increase DNN performance on
various tasks, including picture classification and edge detection.Comment: 19 page
Learning to Generate Training Datasets for Robust Semantic Segmentation
Semantic segmentation techniques have shown significant progress in recent
years, but their robustness to real-world perturbations and data samples not
seen during training remains a challenge, particularly in safety-critical
applications. In this paper, we propose a novel approach to improve the
robustness of semantic segmentation techniques by leveraging the synergy
between label-to-image generators and image-to-label segmentation models.
Specifically, we design and train Robusta, a novel robust conditional
generative adversarial network to generate realistic and plausible perturbed or
outlier images that can be used to train reliable segmentation models. We
conduct in-depth studies of the proposed generative model, assess the
performance and robustness of the downstream segmentation network, and
demonstrate that our approach can significantly enhance the robustness of
semantic segmentation techniques in the face of real-world perturbations,
distribution shifts, and out-of-distribution samples. Our results suggest that
this approach could be valuable in safety-critical applications, where the
reliability of semantic segmentation techniques is of utmost importance and
comes with a limited computational budget in inference. We will release our
code shortly
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