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| Description |
elements of the source code ar e very much important for all the students looking at this website
File list:
license.txt
multisvm.m
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| Related source code |
[Multi-Objective Particle Swarm Optim...] - This package contains the functions for a MOPSO. MOPSO is among the most powerful algorithms in multi-objective optimizations.
[particle swarm optimisation] - particle swarm optimisation code
[UserTimer] - This program is a UserTimer with Android Studio
Users are saved in SQLserver Database
Timer work with registred user
[Segment Iris portion form eye Artif...] - This code manily based on image processing segmenting the iris portion of eye for security purposes
[> compile
Remember to also adjust your path so MATLAB can find iHOG:
>> addpath(genpath('/path/to/ihog'))
If you want to use iHOG in your own project, you can simply drop the iHOG directory into the root of your project.
Inverting HOG
To invert a HOG point, use the 'invertHOG()' function:
>> feat = features(im, 8);
>> ihog = invertHOG(feat);
>> imagesc(ihog); axis image;
Computing the inverse should take no longer than a second for a typical sized image on a modern computer. (It may slower the first time you invoke it as it caches the paired dictionary from disk.)
Visualizing HOG
iHOG has several functions to visualize HOG. The most basic is 'visualizeHOG()':
>> feat = features(im, 8);
>> visualizeHOG(feat);
The above displays a figure with the HOG glyph and the HOG inverse. This visualization is a drop-in replacement for more standard visualizations, and should work with existing code bases.
The de-facto HOG has signed components, unsigned components, as well as texture components. 'dissectHOG()' visualizes each of these components invidually:
>> dissectHOG(feat);
A similar visualization 'spreadHOG()' shows each dimension individually:
>> spreadHOG(feat);
More visualizations are available. Check out the 'visualizations/' folder and read the comments for more.
Learning
We provide a prelearned dictionary in 'pd.mat', but you can learn your own if you wish. Simply call the 'learnpairdict()' function and pass it a directory of images:
>> pd = learnpairdict('/path/to/images/', 1000000, 1000, 5, 5);
The above learns a 5x5 HOG patch paired dictionary with 1000 elements and a training set size of one million window patches. Depending on the size of the problem, it may take minutes or hours to complete.
Bundled Libraries
The iHOG package contains source code from the SPAMS sparse coding toolbox (http://spams-devel.gforge.inria.fr/). We have modified their code to better support 64 bit machines.
In addition, we have included a select few files from the discriminatively trained deformable parts model (http://people.cs.uchicago.edu/~rbg/latent/). We use their HOG computation code and glyph visualization code.
Questions and Comments
If you have any feedback, please write to Carl Vondrick at vondrick@mit.edu.
References
The conference paper for this software is currently under submission. In the mean time, please see our technical report:
[1] Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba. "Inverting and Visualizing Features for Object Detection." Technical Report. 2013."" target="_blank">iHOG: Inverting Histograms of Orient...] - iHOG: Inverting Histograms of Oriented Gradients
This software package contains tools to invert and visualize HOG features. It implements the Paired Dictionary Learning algorithm described in our paper "Inverting and Visualizing Features for Object Detection" [1].
Installation
Before you can use this tool, you must compile iHOG. Execute the 'compile' script in MATLAB to compile the HOG feature extraction code and sparse coding SPAMS toolbox:
$ cd /path/to/ihog
$ matlab
>> compile
Remember to also adjust your path so MATLAB can find iHOG:
>> addpath(genpath('/path/to/ihog'))
If you want to use iHOG in your own project, you can simply drop the iHOG directory into the root of your project.
Inverting HOG
To invert a HOG point, use the 'invertHOG()' function:
>> feat = features(im, 8);
>> ihog = invertHOG(feat);
>> imagesc(ihog); axis image;
Computing the inverse should take no longer than a second for a typical sized image on a modern computer. (It may slower the first time you invoke it as it caches the paired dictionary from disk.)
Visualizing HOG
iHOG has several functions to visualize HOG. The most basic is 'visualizeHOG()':
>> feat = features(im, 8);
>> visualizeHOG(feat);
The above displays a figure with the HOG glyph and the HOG inverse. This visualization is a drop-in replacement for more standard visualizations, and should work with existing code bases.
The de-facto HOG has signed components, unsigned components, as well as texture components. 'dissectHOG()' visualizes each of these components invidually:
>> dissectHOG(feat);
A similar visualization 'spreadHOG()' shows each dimension individually:
>> spreadHOG(feat);
More visualizations are available. Check out the 'visualizations/' folder and read the comments for more.
Learning
We provide a prelearned dictionary in 'pd.mat', but you can learn your own if you wish. Simply call the 'learnpairdict()' function and pass it a directory of images:
>> pd = learnpairdict('/path/to/images/', 1000000, 1000, 5, 5);
The above learns a 5x5 HOG patch paired dictionary with 1000 elements and a training set size of one million window patches. Depending on the size of the problem, it may take minutes or hours to complete.
Bundled Libraries
The iHOG package contains source code from the SPAMS sparse coding toolbox (http://spams-devel.gforge.inria.fr/). We have modified their code to better support 64 bit machines.
In addition, we have included a select few files from the discriminatively trained deformable parts model (http://people.cs.uchicago.edu/~rbg/latent/). We use their HOG computation code and glyph visualization code.
Questions and Comments
If you have any feedback, please write to Carl Vondrick at vondrick@mit.edu.
References
The conference paper for this software is currently under submission. In the mean time, please see our technical report:
[1] Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, Antonio Torralba. "Inverting and Visualizing Features for Object Detection." Technical Report. 2013.
[MODELLING AND SIMULATION OFPHOTOVOLT...] - The work presented in this thesis describes the development of a computeraided design (CAD) package for photovoltaic (PV) arrays. The CAD package,
which is based on the MATLAB software, has been specifically developed to
investigate the performance of, and obtain the characteristics of a twodimensional PV array under partial shading conditions. Partial shading is an
important phenomenon in PV systems as it can lead to multiplicity of
maximum power points (MPP) and complex terminal characteristics, which
complicates the requirements placed on the maximum power point tracking
(MPPT) algorithm. It is therefore, important that the effects of partial shading,
and indeed temperature, on the characteristics of a PV array are studied. The
2 CAD package developed in this work was tested for characterising a PV panel
against the manufacturers’ data sheets. The effects of partial shading on the
characteristics of PV arrays were investigated for a variety of conditions with
particular reference to the complexities partial shading has on the maximum
power tracking strategies. The tests concluded that partial shading and
varying temperatures resulted in reduced outputs and power losses, thereby
affecting the performance of the PV array. The package, which is based on the
two-diode model of a PV module, provides a convenient tool for the PV
designer and/or a trainee.
[Particle swarm optimization] - PSO in matlab
[ConvertIt] - this application is a good conversion , you can with this application convert Area ,distance, Euro ,Time,Mass,Temperature and Volume
[ParticlePlayer] - particular play demo
[certificates] - Ade7758 code , it gives basic description of how to configure ade7758 chip and set values to its registers.
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