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COMPUTER VISION IN
INDUSTRY AND ACADEMIA
Dmytro Mishkin
Czech Technical University in Prague
Clear Research Corporation
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!