Presentation about Matrix and Tensor Tools for Computer Vision http://www.slideshare.net/andrewssobral/matrix-and-tensor-tools-for-computer-vision MTT: Matlab Tensor ...

Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective methods ...

Abstract: With the increasing importance of using carbon fiber reinforced polymer (CFRP) composite in the aircraft industry, it becomes ever more critical to monitor the quality and health of CFRP ...

SISR represents a classical research topic within the field of computer vision, which has garnered significant attention in recent years. The objective of this research is to reconstruct HR images ...

This is the Matlab Package for the Online Sparse Dictionary Learning (OSDL) algorithm, presented in: J. Sulam, B. Ophir, M. Zibulevsky and M. Elad, "Trainlets: Dictionary Learning in High Dimensions," ...

Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ...

In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better ...

Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible ...

Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and ...

The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an ...

Spectral algorithms are widely applied to data clustering problems, including finding communities or partitions in graphs and networks. We propose a way of encoding sparse data using a ...