Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneur and startuppers. Annually it takes place on 2-4 of October in Lviv at the Arena Lviv stadium. In 2015 conference gathered more than 1400 participants and over 100 speakers from companies like Facebook. FitBit, Mail.ru, HP, Epson and IBM. More details about conference at itarene.lviv.ua.

1. COMPUTER VISION IN
INDUSTRY AND ACADEMIA
Dmytro Mishkin
Czech Technical University in Prague
Clear Research Corporation
ducha.aiki@gmail.com

2. 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!

3. 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

4. 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)

5. QUESTIONS?

6. APPENDIX

7. MORE EXAMPLES

8. 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

9. RECOVER BOTTOM LAYERS FROM PAINTING
Scene
Near Infrared Photo
Inner layer
recovery
Result
Tanaka2015, Recovering Inner Slices of Translucent Objects by Multi-frequency Illumination

10. SCENE TIMELAPSE FROM INTERNET PHOTOS
http://grail.cs.washington.edu/projects/timelapse/

11. 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

12. 3D RECONSTRUCTION

13. 3D RECONSTRUCTION
Schonberger2015. From Single Image Query to Detailed 3D Reconstruction
Heinly2015. Reconstructing the World* in Six Days

14. IDENTIFY MATERIAL PROPERTIES FROM VIDEO
Davis2015, Visual Vibrometry: Estimating Material Properties from Small
Motions in Video http://www.visualvibrometry.com/

15. ESTIMATE NUMBER OF PEOPLE ON PHOTO
Idrees2013, Multi-Source Multi-Scale Counting in Extremely Dense Crowd
Imageshttp://crcv.ucf.edu/projects/crowdCounting/index.php

16. MEDICAL CV
Computer Vision for Medical. Imaging. Polina Golland. CSAIL/EEC,
https://courses.csail.mit.edu/6.869

17. 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?!”

18. 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

19. 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!