Displaying 1 to 20 from 21 results

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.

glsl-colormap - A collection of GLSL fragment shaders to draw color maps.


. Each *.frag shader sources provides a colormap function, which takes an float argument x (x should be: 0.0 <= x <= 1.0). The colormap function returns a vec4 value which represents an RGBA color.

imgproc_scripts - Image processing scripts for learning purposes.


Octave or Python scripts to solve some image processing problems. The scripts are just a practice to learn some basic techniques or a first and easier implementation of more complex ones. The implemented algorithm is based on [1], an efficient application of the classical Hough Transform technique to detect ellipses on an image. The main problem of the usage of the normal Hough Transform for ellipse detection is the necessary high dimensional accumulator to store the parameters' votes, since the general equation of an ellipse is composed of 5 variables: the center point [x0 y0], the minor and major half-lengths a and b, and the rotation angle (check this here). Xie's implementation makes some assumptions and can find all parameters with great accuracy using just a 1D accumulator. The complexity is in the number of nonzero pixels of the image.

visual-tracking-matlab - Matlab code for several visual tracking algorithms


This repository contains Matlab code for several visual trackers. As you might imagine the code is mostly used for academic and research purposes, to facilitate experiment repeatability and extensions. Instructions on citing the code in research publicatons are available in subfolders of individual trackers. Other than that the code is available under BSD license unless stated otherwise in the file (some files are imported from other projects).

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).

FEAST - A FEAture Selection Toolbox for C/C+, Java, and Matlab/Octave.


A FEAture Selection Toolbox for C/C++ & MATLAB/OCTAVE, v2.0.0. If you use these implementations for academic research please cite the relevant paper above. All FEAST code is licensed under the BSD 3-Clause License.

MIToolbox - Mutual Information functions for C and MATLAB


MIToolbox contains a set of functions to calculate information theoretic quantities from data, such as the entropy and mutual information. The toolbox contains implementations of the most popular Shannon entropies, and also the lesser known Renyi entropy. The toolbox also provides implementations of the weighted entropy and weighted mutual information from "Information Theory with Application", S. Guiasu (1977). The toolbox only supports discrete distributions, as opposed to continuous. All real-valued numbers will be processed by x = floor(x). These functions are targeted for use with feature selection algorithms rather than communication channels and so expect all the data to be available before execution and sample their own probability distributions from the data.

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.