Thursday, August 24, 2017
Numeric Analysis
[1] [Book] Computational fluid mechanics and heat transfer
http://inis.jinr.ru/sl/Simulation/Tannehill,_CFM_and_Heat_Transfer,2_ed/
Friday, August 11, 2017
Monday, August 07, 2017
Saturday, August 05, 2017
Wednesday, July 26, 2017
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
Tuesday, July 18, 2017
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/
Sunday, July 09, 2017
Saturday, July 08, 2017
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
Sunday, July 02, 2017
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/
Monday, June 19, 2017
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
Wednesday, June 14, 2017
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
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
Friday, June 09, 2017
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
Wednesday, June 07, 2017
Monday, June 05, 2017
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
Thursday, May 18, 2017
Wednesday, May 10, 2017
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/
Subscribe to:
Posts (Atom)
Posts
