Displaying 1 to 20 from 75 results

PRMLT - Matlab code for machine learning algorithms in book PRML

This package is a Matlab implementation of the algorithms described in the classical machine learning textbook: Pattern Recognition and Machine Learning by C. Bishop (PRML). Note: this package requires Matlab R2016b or latter, since it utilizes a new syntax of Matlab called Implicit expansion (a.k.a. broadcasting in Python).

Image-feature-detection-using-Phase-Stretch-Transform - PST or Phase Stretch Transform is an operator that finds features in an image

Phase Stretch Transform (PST) is an operator that finds features in an image. PST takes an intensity image I as its input, and returns a binary image out of the same size as I, with 1's where the function finds sharp transitions in I and 0's elsewhere. PST function is also able to return the detected features in gray scale level (i.e. without thresholding). This function is provided for research purposes only. A license must be obtained from the University of California, Los Angeles for any commercial applications. The software is protected under a US patent.

Freemat - A Matlab alternative

FreeMat is a free environment for rapid engineering and scientific prototyping and data processing. It is similar to commercial systems such as MATLAB and IDL. It has built in arithmetic for manipulation of all supported data types, N-dimensional array manipulation, 2D and 3D plotting and image display, Visualization, Image manipulation, and as well as parallel programming.

matlab2tikz - This program converts MATLAB®/Octave figures to TikZ/pgfplots figures for smooth integration into LaTeX

matlab2tikz is a MATLAB(R) script to convert native MATLAB(R) figures to TikZ/Pgfplots figures that integrate seamlessly in LaTeX documents. To download the official releases and rate matlab2tikz, please visit its page on FileExchange.

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

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.

Multi Touch Digit OCR With Matlab Neural Network Wpf Project

Multi Touch Digit OCR Project is a wpf project that works on multi touch devices but it works well on normal devices , this project uses matlab core , that creates 4 feed forward neural network and train them with Back Propagation Algorithm for detecting numbers that you draw .

Matlab .NET Bridge Framework

The Matlab .NET Bridge is a managed code wrapper around the C Matlab engine API. It is designed to offer an interface that feels right when being called from managed languages.

gramm - Gramm is a complete data visualization toolbox for Matlab

Gramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library by Hadley Wickham. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab. Gramm is a data visualization toolbox for Matlab that allows to produce publication-quality plots from grouped data easily and flexibly. Matlab can be used for complex data analysis using a high-level interface: it supports mixed-type tabular data via tables, provides statistical functions that accept these tables as arguments, and allows users to adopt a split-apply-combine approach (Wickham 2011) with rowfun(). However, the standard plotting functionality in Matlab is mostly low-level, allowing to create axes in figure windows and draw geometric primitives (lines, points, patches) or simple statistical visualizations (histograms, boxplots) from numerical array data. Producing complex plots from grouped data thus requires iterating over the various groups in order to make successive statistical computations and low-level draw calls, all the while handling axis and color generation in order to visually separate data by groups. The corresponding code is often long, not easily reusable, and makes exploring alternative plot designs tedious.

extended-berkeley-segmentation-benchmark - Extended version of the Berkeley Segmentation Benchmark [1] used for evaluation in [2]

A more comprehensive benchmark can now be found at davidstutz/superpixel-benchmark.This is an extended version of the Berkeley Segmentation Benchmark, available here and introduced in [1], used to assess superpixel algorithms.

matlab-mnist-two-layer-perceptron - A two layer perceptron implemented in MatLab to recognize handwritten digits based on the MNIST dataset

In course of a seminar on “Selected Topics in Human Language Technology and Pattern Recognition”, I wrote a seminar paper on neural networks: "Introduction to Neural Networks". The seminar paper and the slides of the corresponding talk can be found in my blog article: Seminar Paper “Introduction to Neural Networks”. Background on neural networks and the two-layer perceptron can be found in my seminar paper.Update: The code can be adapted to allow mini-batch training as done in this fork.

whisk - Fully automated tracking of single rows of whiskers in high-speed video.

A description of this software as well as detailed instructions and a tutorial may be found here. Pre-built binaries are available for download.

mexplus - C++ Matlab MEX development kit.

C++ Matlab MEX development kit. The kit contains a couple of C++ classes and macros to make MEX development easy in Matlab. There are 3 major components in the development kit.

facerecognition_guide - This is a guide to face recognition with Python, GNU Octave/MATLAB and OpenCV2 C++

This is my guide to face recognition with OpenCV2 C++ and GNU Octave/MATLAB. If you research on face recognition, you'll soon notice there's a gigantic number of publications, but source code is very sparse. So this guide is here to change that. Two algorithms are explained and implemented with GNU Octave/MATLAB and OpenCV2 C++ namely Eigenfaces and Fisherfaces. To build the Python version of this document simply run make python, to build the Octave version of this document run make octave.

glider_toolbox - MATLAB/Octave scripts to manage data collected by a glider fleet, including data download, data processing and product and figure generation, both in real time and delayed time

The glider toolbox is a set of MATLAB/Octave scripts and functions developed at SOCIB to manage the data collected by a glider fleet. They cover the main stages of the data management process both in real time and delayed time mode: metadata aggregation, data download, data processing, and generation of data products and figures. The toolbox is exhaustively self-documented using the standard documentation comment system. Hence the help pages are available using the documentation browser or the help command.

segyio - Fast Python library for SEGY files.

Segyio is a small LGPL licensed C library for easy interaction with SEG-Y formatted seismic data, with language bindings for Python and Matlab. Segyio is an attempt to create an easy-to-use, embeddable, community-oriented library for seismic applications. Features are added as they are needed; suggestions and contributions of all kinds are very welcome. To catch up on the latest development and features, see the changelog. To write future proof code, consult the planned breaking changes.

MATLAB-Online - MATLAB Online Toolbox - Create interactive charts in your web browser with MATLAB and Plotly

The latest version of the wrapper can be downloaded here. Once downloaded, run plotlysetup('your_username', 'your_api_key') to get started.

WBC_Matlab - Efficient Wasserstein Barycenter in MATLAB (for "Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support" TSP)

If you need more efficient and scalable implementation in MPI and C (patent pending), please contact the author. Jean-David Benamou, Guillaume Carlier, Marco Cuturi, Luca Nenna, and Gabriel Peyré, "Iterative Bregman projections for regularized transportation problems." SIAM Journal on Scientific Computing 37.2 (2015): A1111-A1138.

kafbox - A Matlab benchmarking toolbox for kernel adaptive filtering

A Matlab benchmarking toolbox for kernel adaptive filtering. Kernel adaptive filters are online machine learning algorithms based on kernel methods. Typical applications include time-series prediction, nonlinear adaptive filtering, tracking and online learning for nonlinear regression. This toolbox includes algorithms, demos, and tools to compare their performance.

kmbox - Kernel Methods Toolbox for Matlab/Octave

The Kernel Methods Toolbox (KMBOX) is a collection of MATLAB programs that implement kernel-based algorithms, with a focus on regression algorithms and online algorithms. It can be used for nonlinear signal processing and machine learning. KMBOX includes implementations of algorithms such as kernel principal component analysis (KPCA), kernel canonical correlation analysis (KCCA) and kernel recursive least-squares (KRLS).