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Found 17 papers, 1 papers with code
Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance.
Painting Analysis Using Wavelets and Probabilistic Topic Models
In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone.
X-ray image separation via coupled dictionary learning
In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings.
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets
We propose an image representation scheme combining the local and nonlocal characterization of patches in an image.
Multi-modal dictionary learning for image separation with application in art investigation
Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement.
Recursive Diffeomorphism-Based Regression for Shape Functions
This paper proposes a recursive diffeomorphism based regression method for one-dimensional generalized mode decomposition problem that aims at extracting generalized modes $\alpha_k(t)s_k(2\pi N_k\phi_k(t))$ from their superposition $\sum_{k=1}^K \alpha_k(t)s_k(2\pi N_k\phi_k(t))$.
LDMNet: Low Dimensional Manifold Regularized Neural Networks
To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features.
Gaussian Process Landmarking on Manifolds
As a means of improving analysis of biological shapes, we propose an algorithm for sampling a Riemannian manifold by sequentially selecting points with maximum uncertainty under a Gaussian process model.
Robust and Resource Efficient Identification of Shallow Neural Networks by Fewest Samples
In the case of the shallowest feed-forward neural network, second order differentiation and tensors of order two (i. e., matrices) suffice as we prove in this paper.
Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning
Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed.
PiPs: a Kernel-based Optimization Scheme for Analyzing Non-Stationary 1D Signals
This paper proposes a novel kernel-based optimization scheme to handle tasks in the analysis, e. g., signal spectral estimation and single-channel source separation of 1D non-stationary oscillatory data.
Expression of Fractals Through Neural Network Functions
The combination of this phenomenon with the capacity, demonstrated here, of DNNs to efficiently approximate IFS may contribute to the success of DNNs, particularly striking for image processing tasks, as well as suggest new algorithms for representing self similarities in images based on the DNN mechanism.
Approximating the Riemannian Metric from Point Clouds via Manifold Moving Least Squares
In this paper, we investigate the convergence of such approximations made by Manifold Moving Least-Squares (Manifold-MLS), a method that constructs an approximating manifold $\mathcal{M}^h$ using information from a given point cloud that was developed by Sober \& Levin in 2019.
Image Separation with Side Information: A Connected Auto-Encoders Based Approach
These features are then used to (1) reproduce both of the original RGB images, (2) reconstruct the hypothetical separated X-ray images, and (3) regenerate the mixed X-ray image.
Neural Network Approximation of Refinable Functions
Namely, we show that refinable functions are approximated by the outputs of deep ReLU networks with a fixed width and increasing depth with accuracy exponential in terms of their number of parameters.
Mixed X-Ray Image Separation for Artworks with Concealed Designs
In this paper, we focus on X-ray images of paintings with concealed sub-surface designs (e. g., deriving from reuse of the painting support or revision of a composition by the artist), which include contributions from both the surface painting and the concealed features.
Diffusion Maps : Using the Semigroup Property for Parameter Tuning
The DM procedure consists in constructing a spectral parametrization for the manifold from simulated random walks or diffusion paths on the data set.
