COMPUTER VISION IN

INDUSTRY AND ACADEMIA

Dmytro Mishkin

Czech Technical University in Prague

Clear Research Corporation

ducha.aiki@gmail.com

AGENDA

1. What are current applications?

2. What the difference between CV in academia

and industry?

3. What you can do?

4. What can help you with it?

Computer Vision is much

closer than it appears!

COURSES

CTU in Prague:

https://cw.fel.cvut.cz/wiki/courses/ae4m33mpv/start

Stanford:

http://vision.stanford.edu/teaching/cs131_fall1415/index.html

http://vision.stanford.edu/teaching/cs223b/

http://cs231n.stanford.edu/

Brown University:

http://cs.brown.edu/courses/cs143

LIBRARIES

(MOSTLY C++ AND PYTHON…)

OpenCV (everything, lots of languages)

VLFeat (pain plain C + Matlab… )

Caffe (Deep Learning)

PCL (Point Cloud)

SimpleCV (Python and really simple)

skilit-learn (Python… yes, it is not computer

vision)

QUESTIONS?

APPENDIX

MORE EXAMPLES

COMPUTER VISION IS ABLE TO

identify criminal by gait. Convicted Anna Lindh

murderer in 2003

Lynnerup et al., 2007: Identification by facial recognition, gait analysis and photogrammetry: The Anna Lindh murder

Makihara et al., 2015. Gait Recognition: Databases, Representations, and Applications

RECOVER BOTTOM LAYERS FROM PAINTING

Scene

Near Infrared Photo

Inner layer

recovery

Result

Tanaka2015, Recovering Inner Slices of Translucent Objects by Multi-frequency Illumination

SCENE TIMELAPSE FROM INTERNET PHOTOS

http://grail.cs.washington.edu/projects/timelapse/

DETECT AND RECOGNIZE TEXT IN WILD

Neumann2015, Efficient Scene Text Localization and Recognition with Local Character Refinement

Jaderberg2014, Reading Text in the Wild with Convolutional Neural Networks

3D RECONSTRUCTION

3D RECONSTRUCTION

Schonberger2015. From Single Image Query to Detailed 3D Reconstruction

Heinly2015. Reconstructing the World* in Six Days

IDENTIFY MATERIAL PROPERTIES FROM VIDEO

Davis2015, Visual Vibrometry: Estimating Material Properties from Small

Motions in Video http://www.visualvibrometry.com/

ESTIMATE NUMBER OF PEOPLE ON PHOTO

Idrees2013, Multi-Source Multi-Scale Counting in Extremely Dense Crowd

Imageshttp://crcv.ucf.edu/projects/crowdCounting/index.php

MEDICAL CV

Computer Vision for Medical. Imaging. Polina Golland. CSAIL/EEC,

https://courses.csail.mit.edu/6.869

CV IN ACADEMY

Yann LeCun, Facebook (Deep Learning and Computer

Vision guy) : “We nailed them!”

John Leonard, MIT (Robotics guy):

“75% accuracy is what you call “nailed?!”

ROBOTICS: NEEDS 99.999% ACCURACY

If you have 1% error rate and examine road

every second:

You will be wrong every 10 minutes

1 − 0.9960𝑠𝑒𝑐∗10𝑚𝑖𝑛 = 0.997 % probability of failure

SIMPLE IMPLEMENTATION

NO CV ENGINEER NEEDED!

1. Describe all database photos by Imagenet CNN

(Caffe library)

2. Put them in kd-tree (OpenCV)

3. Describe current camera output

4. Query kd-tree

5. ????

6. PROFIT!