Displaying 1 to 8 from 8 results

lrslibrary - Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

  •    Matlab

Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos. The LRSLibrary provides a collection of low-rank and sparse decomposition algorithms in MATLAB. The library was designed for motion segmentation in videos, but it can be also used (or adapted) for other computer vision problems (for more information, please see this page). Currently the LRSLibrary offers more than 100 algorithms based on matrix and tensor methods. The LRSLibrary was tested successfully in several MATLAB versions (e.g. R2014, R2015, R2016, R2017, on both x86 and x64 versions). It requires minimum R2014b.

ITensor - A C++ library for rapidly creating efficient tensor network calculations

  •    C++

An efficient and flexible C++ library for performing tensor network calculations. The foundation of the library is the Intelligent Tensor or ITensor. Contracting ITensors is no harder than multiplying scalars: matching indices automatically find each other and contract. This makes it easy to transcribe tensor network diagrams into correct, efficient code.

ostd - Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video Sequences

  •    Matlab

Background subtraction is an important task for visual surveillance systems. However, this task becomes more complex when the data size grows since the real-world scenario requires larger data to be processed in a more efficient way, and in some cases, in a continuous manner. Until now, most of background subtraction algorithms were designed for mono or trichromatic cameras within the visible spectrum or near infrared part. Recent advances in multispectral imaging technologies give the possibility to record multispectral videos for video surveillance applications. Due to the specific nature of these data, many of the bands within multispectral images are often strongly correlated. In addition, processing multispectral images with hundreds of bands can be computationally burdensome. In order to address these major difficulties of multispectral imaging for video surveillance, this paper propose an online stochastic framework for tensor decomposition of multispectral video sequences (OSTD). The source code is available only for academic/research purposes (non-commercial).




tf-decompose - Tensor decomposition implemented in TensorFlow

  •    Python

CP and Tucker tensor decompositions implemented in TensorFlow. Preliminary results: with sensory bread data, TuckerTensor.hosvd seems to perform quite poorly, while TuckerTensor.hooi and DecomposedTensor.train_als learn reconstructions with fit ~0.70.

hottbox - HOTTBOX: Higher Order Tensors ToolBOX.

  •    Python

Welcome to the toolbox for tensor decompositions, statistical analysis, visualisation, feature extraction, regression and non-linear classification of multi-dimensional data. This will allow you to modify the source code in the way it will suit your needs. Additionally, you will be on top of the latest changes and will be able to start using new stable features which are located on develop branch until the official release. The list of provisional changes for the next release (and the CI status) can be also be found on develop branch in CHANGELOG file.

hottbox-tutorials - HOTTBOX: Higher Order Tensors ToolBOX. Tutorials

  •    Jupyter

This repository contains a series of tutorials on how to use hottbox. All data for these tutorials can be found under data/ directory.






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