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Found 10 papers, 4 papers with code
In-Flight Estimation of Instrument Spectral Response Functions Using Sparse Representations
Accurate estimates of Instrument Spectral Response Functions (ISRFs) are crucial in order to have a good characterization of high resolution spectrometers.
Learning grounded word meaning representations on similarity graphs
This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy.
Graph Constrained Data Representation Learning for Human Motion Segmentation
Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS).
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding
The main advantage of DGE over state-of-the-art self-supervised approaches is that it does not require any training set, but instead learns iteratively from the data itself a low-dimensional embedding that reflects their temporal and semantic similarity.
Enhancing temporal segmentation by nonlocal self-similarity
Temporal segmentation of untrimmed videos and photo-streams is currently an active area of research in computer vision and image processing.
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing.
Nonnegative Matrix Factorization with Transform Learning
Traditional NMF-based signal decomposition relies on the factorization of spectral data, which is typically computed by means of short-time frequency transform.
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to many applications.
Bayesian estimation of the multifractality parameter for image texture using a Whittle approximation
Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge.
