Web Ftp Online

Sites
[1] https://www.onlineftp.ch/

PDF Signature

Resources:
[1] Make signature and put over pdf  https://www.pdfbuddy.com
[2] Draw signature over pdf https://www.pdffiller.com

Machine Learning Lectures & Tools

References:

[1] Clustering notes: http://www.cs.toronto.edu/~mbrubake/teaching/C11/Handouts/Clustering.pdf

Datasets:


EMOTIV EPOC+ 14 Channel Mobile EEG https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State#
https://archive.ics.uci.edu/ml/datasets

Resources:

[1] Optimizing parameters of Classifier https://weka.wikispaces.com/Optimizing+parameters
[2] CSV to ARFF online http://ikuz.eu/csv2arff/

VMWare Player Enter Setup

bios.forceSetupOnce = "TRUE"
bios.bootDelay = "5000"

PCA Feature extraction

References:
[1] PCA http://www.visiondummy.com/2014/05/feature-extraction-using-pca/
[2] Reducción de dimensinalidad usando PCA   https://www.coursera.org/learn/clasificacion-imagenes/lecture/PaTVm/reduccion-de-descriptores-pca


[3] Opencv code with explanation for dimentional reduction
    https://stackoverflow.com/questions/27733002/how-to-use-pca-to-reduce-dimension
[4] Distances http://wwwae.ciemat.es/~cardenas/docs/lessons/MedidasdeDistancia.pdf

Distances

References
[1] http://wwwae.ciemat.es/~cardenas/docs/lessons/MedidasdeDistancia.pdf

Lectures Descriptors & datasets

References:

1) BoW summary https://prateekvjoshi.com/2014/08/17/image-classification-using-bag-of-words-model/

2) https://github.com/constanton/bLDFV



Datasets

0) 2D hela https://ome.grc.nia.nih.gov/iicbu2008/hela/index.html
    2D/3D Hela http://murphylab.web.cmu.edu/data/
1) Biomedical flourcence images
http://mivia.unisa.it/datasets/biomedical-image-datasets/hep2-image-dataset/

2) Microscopic/ Histology, Brain, Retinal and more https://sites.google.com/site/lisaywtang/research/descriptors

3) Other for visualization https://grouplens.org/datasets/movielens/ 

Animes online

Bleach
1) Sub English https://www4.animesubhd.net/watch/episode/subbed/bleach-238/
2) Sub Spanish http://jkanimeonline.com/ver/bleach-226.html

Bag of Features and Texture

Notes

What is the difference between SIFT and Dense SIFT

*SIFT consists of both detection and description while dense sift only uses the descriptor in densely sampled locations [1].

*SIFT identifies interest points using Difference of Gaussian Filtering (DoG) before using Histogram of Oriented Gradients (HOG) to describe these interest points, however Dense-SIFT does not identify interest points, it simply divides the image into overlapping cells before using HOG to describe them. since they both use HOG they both produce 128 dimensional feature vectors [1].

*SIFT is typically computed at interest points. Dense SIFT is computed at every pixel, or every kth pixel. HOG is computed for a rectangular cell array where each cell is usually 8x8 pixels. Dense SIFT and HOG are similar in the sense that they both characterize edginess and orientation around pixels, but the computations are different. Jianxiong Xiao's 2x2 HOG is different than normal HOG. The truth is that once you know how these kinds if features work you can get fancy and histogram them differently, change normalization terms, etc and create your own variant. I spoke with Prof Xiao many times about this when we ovarlapped at MIT [2].

*Firstly, Difference of Gaussians (DoG) can be used for estimating Laplacian of Gaussians (LoG), which are useful for finding edges and blobs. DoG is computationally faster so it is used. Overall, the way in which LoG is used for SIFT and HOG is the fundamental difference between these two feature descriptors. Dense SIFT is exactly as it sounds, SIFT computed densely for every pixel in the image and it helps in image registration, pose estimation, object recognition, etc [2].

Resources

1) BoF imlmentation using SURF, IHOG http://www.cvc.uab.cat/~aldavert/plor/software.html

2) Texture video https://www.youtube.com/watch?v=LQBKIi-Xtbc

3) Textons http://webpages.uncc.edu/~yjaved/publications.html


4) Bag of Visual Words implementation (Functional) http://www.codeproject.com/Articles/619039/Bag-of-Features-Descriptor-on-SIFT-Features-with-O

5) Gabor filters histogram, explanation
    http://stackoverflow.com/questions/20608458/gabor-feature-extraction   
6) Filter Banks, Matlab Source Code
    http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html

7) Texture classification using textons  http://courses.media.mit.edu/2008fall/mas622j/Projects/NickLoomis/


References
 [1] https://www.researchgate.net/post/What_is_the_difference_between_SIFT_and_Dense_SIFT
 [2] https://www.quora.com/Computer-Vision-Is-there-a-difference-if-any-between-dense-SIFT-and-HOG

Self-Organizing Map (Resources)

Source code:

1) https://www.codeproject.com/Articles/21385/Kohonen-s-Self-Organizing-Maps-in-C-with-Applicati
2) https://www.codeproject.com/Articles/16273/Self-Organizing-Feature-Maps-Kohonen-maps
3) C++ Sample http://mnemstudio.org/neural-networks-som1.htm
4) GHSOM https://github.com/fffaraz/GHSOM-CPP

Yellow dots, Microdots, how to discover

Resources

1) Tutorial http://blog.erratasec.com/2017/06/how-intercept-outed-reality-winner.html
2) DocuColor Tracking Dot Decode Guide https://w2.eff.org/Privacy/printers/docucolor

Programming books

Resources

1) Ansi C http://www3.fi.mdp.edu.ar/electronica/catedras/ddcm/files/The_C_Programming_Language_KandR.pdf

Convert PowerPoint to EPS

When you draw some design and you like to put in your paper

1. Open your design on Powerpoint, then save as .wmf(windows meta file)
2. go to https://cloudconvert.com/anything-to-eps
3. Load you .wmf file and convert to eps
4. Download result

Persian Language

Courses

1) http://www.persianlanguageonline.com/learn/beginner
2) https://persianlanguage.anu.edu.au
3) https://www.nyu.edu/gsas/dept/mideast/virtualpersian/index.html

Fiducial Land Mark (Face Keypoints)

Facial Keypoints
[1] https://github.com/sunsided/facial-keypoints
[2] kaggle https://www.kaggle.com/c/facial-keypoints-detection/data


Resources:
1) Source Code Matlab/C++
http://cmp.felk.cvut.cz/~uricamic/flandmark/

Autodominio

Recursos
1) http://www.ayuda-psicologia.org/2013/12/salirse-siempre-con-la-suya-o-el.html

LBP Descriptor resources

Lectures

Texture descriptor (pt) http://wiki.icmc.usp.br/images/d/d7/Dip10_imagedescription-texture.pdf



Resources

1) 2015 LBP variant library https://github.com/carolinepacheco/lbplibrary
2) University OULU  http://www.cse.oulu.fi/CMV/Downloads/LBPMatlab
3) Upsala University http://www.cb.uu.se/~gustaf/textureDescriptors/

Different kind of Image Medical Datasets

References:
[1] Neuromorph https://datamed.org/repository_list.php
[2] http://neuroangio.org/anatomy-and-variants/anterior-cerebral-artery/

Tools for text to speech

[1] use TensorFlow http://hts.sp.nitech.ac.jp/?Download#zf6c5f7c

Common datasets for image processing and computer vision

Resources:

[1] Hela https://ome.grc.nia.nih.gov/iicbu2008/hela/index.html