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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.

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.

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.