OpenCV2 in Python for Windows

1.   Below Python packages are to be downloaded and installed to their default locations.
  Python-2.7.x.
  Numpy.
  Matplotlib (Optional).
2.    Install all packages into their default locations. Python will be installed to C:/Python27/.
3.    After installation, open Python IDLE. Enter import numpy and make sure Numpy is working fine.
4.    Download latest OpenCV release from sourceforge site and double-click to extract it.
5.    Goto opencv/build/python/2.7
       Copy cv2.pyd to C:/Python27/lib/site-packages.
6.  Test
>>> import cv2
>>> print cv2.__version__

Installing Tensor Flow (Problems)

Main guide
https://www.tensorflow.org/install/install_sources

Cannot find bazel. Please install bazel.
Solution https://docs.bazel.build/versions/master/install-ubuntu.html

Monitoring Nvidia card

$watch -n 1 nvidia-smi #to obtain continuous updates without filling the terminal with output

$sudo pip install gpustat
$gpustat
 
$sudo pip install glances
$sudo pip install glances[gpu]
$glances

Resources:
[1] Cuda 7.5 http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda_7.5.18_linux.run
[2] Install Cuda 7.5 http://www.r-tutor.com/gpu-computing/cuda-installation/cuda7.5-ubuntu

Computer Vision Laboratories & Courses

Labs:

Computer Vision Lab http://vision.ece.ucsb.edu/

Oxford https://www.robots.ox.ac.uk/~vgg/projects.html

University Central of Florida (Computer Vision -crowds dataset) http://vision.eecs.ucf.edu/

Courses:

1) Computer Vision: Algorithms and Applications http://szeliski.org/Book/ (2017)
2) University of California https://cseweb.ucsd.edu/classes/sp16/cse152-a/
3) Computer vision course https://courses.cs.washington.edu/courses/cse455/09wi/Lects/

Journals for Bio Informatics

https://scfbm.biomedcentral.com/track/pdf/10.1186/1751-0473-3-6?site=scfbm.biomedcentral.com

springer computer vision http://www.springer.com/computer/image+processing/journal/11263  11 8.2

IEEE http://signalprocessingsociety.org/publications-resources/ieee-transactions-image-processing 44 4.3
ELSEVIER Pattern recognition https://www.journals.elsevier.com/pattern-recognition/ 47 4.5
ELSEVIER Medical image analysis https://www.journals.elsevier.com/medical-image-analysis/ 56 4.1
IEEE Medical images https://ieee-tmi.org/ 68 3.9
ELSEVIER https://www.journals.elsevier.com/computer-vision-and-image-understanding/ 112 3.2
ELSEVIER https://www.journals.elsevier.com/image-and-vision-computing/ 165 2.6
ELSEVIER https://www.journals.elsevier.com/computer-vision-and-image-understanding/ 189 2.4



http://www.guide2research.com/journals/computer-vision

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/ 

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

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/

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/

Common datasets for image processing and computer vision

Resources:

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

OpenCV 2.4 Fix cvCreateGLCM()

Changes for cvCreateGLCM(), see [1] for more details, and [2] has code ready for recompile.

Resources/References:

  [1] http://intercontineo.com/article/723743881/

  [2] https://drive.google.com/open?id=0B8teK-3L4sK2Tll1Y2FyWmNCc3M

Waldboost/LBP Detector

Resources:
1) https://github.com/mmaci/vutbr-fit-waldboost-detector

Tracking Resources

Resources:
1. https://github.com/gnebehay/OpenTLD
2. https://github.com/gnebehay/VOTR
3. https://github.com/gnebehay/CppMT

Shape Matching

Resources:

[1]  Shape Indexing and Matching Using Shock Graphs
  http://www.cs.toronto.edu/~dmac/source_code.html
[2]  Skeletonization
     http://www.inf.u-szeged.hu/~palagyi/skel/skel.html
[3] K3M
   http://matwbn.icm.edu.pl/ksiazki/amc/amc20/amc2029.pdf

Conferences

http://www.visigrapp.org

Lectures

 Overview (Pt)
http://iris.sel.eesc.usp.br/sel886/RANSAC_AnOverview.pdf

Overview 2 (En)
http://cmp.felk.cvut.cz/cmp/courses/Y33ROV/Y33ROV_ZS20082009/Lectures/RANSAC/ransac.pdf

Ransac Family (CVPR2011)
http://www.imgfsr.com/CVPR2011/Tutorial6/RANSAC_CVPR2011.pdf

Ransac in Matlab
http://old.vision.ece.ucsb.edu/~zuliani

------------------- ------------------- ------------------- ------------------- ------------------- -------------------

Image Registration CVPR2011
http://www.imgfsr.com/CVPR2011/Tutorial6/

Thin plate spline (Demo)

Definition(summary)
https://en.wikipedia.org/wiki/Thin_plate_spline

Code
http://elonen.iki.fi/code/tpsdemo/index.html

OpenCV 2.4.x over Ubuntu

I installed Opencv 2.4.8 over ubuntu 12.04, works good with images, but i getting error at display/read videos, then i research and use other resources:

[1], is best for me, have scripts for install opencv library, if you like more details about steps, is possible use [2] or [3].


-----------------------------------------

When you like java libraries
Need to do[4]:

$ export JAVA_HOME=/root/jdk1.7.0_07
$ mkdir build
$ cd build
$ cmake -DBUILD_SHARED_LIBS=OFF ../

If you get this message: libv4l2.so.0: error adding symbols: DSO missing from command line collect2, do that:

$grep -rl -- -lv4l1 samples/* modules/* | xargs sed -i 's/-lv4l1/-lv4l1 -lv4l2/g'

If not works, start again using [5].

Resources:

[1] https://help.ubuntu.com/community/OpenCV
[1.1]  https://github.com/jayrambhia/Install-OpenCV/tree/master/Ubuntu/2.4
[2] http://abhitak.wordpress.com/2009/08/29/installing-opencv-on-linux-ubuntu-9-04/
[3] http://www.ozbotz.org/opencv-installation/
[4] http://www.giuseppeurso.eu/en/how-to-compile-opencv-on-centos-with-java-support/
[5] http://www.sysads.co.uk/2014/05/install-opencv-2-4-9-ubuntu-14-04-13-10/