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Computer vision, machine, and deep learning

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Igi Ardiyanto
Igi Ardiyanto

This document provides an overview of computer vision, machine learning, and deep learning with Python. It introduces computer vision and some example applications like optical character recognition and face detection. It then discusses machine learning and how it can be applied to computer vision problems. Deep learning is introduced as a type of machine learning using artificial neural networks. Examples of successful deep learning applications are presented, including speech recognition and the AlphaGo program that mastered the game of Go. Finally, Python is discussed as a programming language well-suited for scientific and deep learning applications due to supporting libraries like NumPy, Scipy, and Matplotlib.

Engineering
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COMPUTER VISION, MACHINE, AND DEEP LEARNING WITH PYTHON Dr.Eng. Igi Ardiyanto
PROFILE Igi Ardiyanto Field of Interest : Robotics Computer Vision Intelligent Transportation System Embedded System Parallel Computing Deep Learning More Information ?? http://te.ugm.ac.id/~igi
What is Computer Vision? Computer Vision, Machine, and Deep Learning with Python
COMPUTER VISION Make computers understand images and video What kind of scene? Where are the people? How far is the building? Where is Waldo? Like when human “sees” something …..
VISION IS REALLY HARD  Vision is an amazing feat of natural intelligence  Visual cortex occupies about 50% of Macaque brain  More human brain devoted to vision than anything else Sik…sik…. Iki dolanan opo panganan, cuk?
OPTICAL CHARACTER RECOGNITION (OCR) Digit recognition, AT&T labs http://www.research.att.com/~yann/ Technology to convert scanned docs to text • If you have a scanner, it probably came with OCR software License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
FACE DETECTION  Many new digital cameras now detect faces  Canon, Sony, Fuji, …
SMILE DETECTION Sony Cyber-shot® T70 Digital Still Camera
What is Machine Learning? Computer Vision, Machine, and Deep Learning with Python
MACHINE LEARNING  Machine learning is programming computers to optimize a performance criterion using example data or past experience.  There is no need to “learn” to calculate payroll  Learning is used when:  Human expertise does not exist (navigating on Mars),  Humans are unable to explain their expertise (speech recognition)  Solution changes in time (routing on a computer network)  Solution needs to be adapted to particular cases (user biometrics)
COMPUTER VISION MEETS MACHINE LEARNING Dog Cat Raccoon Dog Train: Deploy: Training Labels Training Image Features Prediction Image Features Learned model
IMAGE FEATURES ??  Color  Histograms  Shape  … Slide credit: L. Lazebnik
VERY BRIEF TOUR OF SOME CLASSIFIERS  K-nearest neighbor  SVM  Boosted Decision Trees  Neural networks  Naïve Bayes  Bayesian network  Gaussian Logistic regression  Random Forests  RBMs  Etc.
FACIAL ATTRACTIVENESS PREDICTION Yoona: Score 3.6 Yuri: Score 3.4 Tiffany: Score 3.8
FACIAL ATTRACTIVENESS PREDICTION https://github.com/avisingh599/face-rating Yoona: Score 3.6 Yuri: Score 3.4 Tiffany: Score 3.8
What is Deep Learning? Computer Vision, Machine, and Deep Learning with Python
1) A host of statistical machine learning techniques 2) Enables the automatic learning of feature hierarchies 3) Generally based on artificial neural networks DEEP LEARNING
 English and Mandarin speech recognition  Transition from English to Mandarin made simpler by end-to-end DL  No feature engineering or Mandarin-specificsrequired  More accurate than humans  Error rate 3.7% vs. 4% for human tests http://arxiv.org/abs/1512.02595 END-TO-END DEEP LEARNING FOR ENGLISH AND MANDARIN SPEECH RECOGNITION BAIDU DEEP SPEECH 2
FIRST COMPUTER PROGRAM TO BEAT A HUMAN GO PROFESSIONAL Training DNNs : 3 weeks, 340 million training steps on 50 GPUs Play : Asynchronousmulti-threadedsearch Simulations on CPUs, policy and value DNNs in parallel on GPUs Single machine: 40 search threads, 48 CPUs, and 8 GPUs Distributed version: 40 search threads, 1202 CPUs and 176 GPUs Outcome: Beat both European and World Go champions in best of 5 matches ALPHA-GO
DEEP LEARNING EVERYWHERE INTERNET & CLOUD Image Classification Speech Recognition Language Translation Language Processing Sentiment Analysis Recommendation MEDIA & ENTERTAINMENT Video Captioning Video Search Real Time Translation AUTONOMOUS MACHINES Pedestrian Detection Lane Tracking Recognize Traffic Sign SECURITY & DEFENSE Face Detection Video Surveillance Satellite Imagery MEDICINE & BIOLOGY Cancer Cell Detection Diabetic Grading Drug Discovery
So what’s the f*** there for Python? Computer Vision, Machine, and Deep Learning with Python
WHAT IS PYTHON?  General purpose interpreted programming language  Widely used by scientists and programmers of all stripes  Supported by many 3rd-party libraries (currently 21,054 on the main python package website)  Free!
WHY IS IT WELL-SUITED TO SCIENCE?  NumPy  Numerical library for python  Written in C, wrapped by python  Fast  Scipy  Built on top of NumPy (i.e. Also fast!)  Common maths, science, engineering routines  Matplotlib  Hugely flexible plotting library  Similar syntax to Matlab  Produces publication-quality output
WHY IS PYTHON BETTER THAN WHAT I USE NOW?  It can do everything  Fast mathematical operations  Easy file manipulation  Format conversion  Plotting  Scripting  Command line  OK, not everything  Write thesis for you
Python has a wide range of deep learning-related libraries available Low level High level (efficient gpu-powered math) (theano-wrapper, models in python code, abstracts theano away) (wrapper for theano, yaml, experiment-oriented) (computer-vision oriented DL framework, model-zoo, prototxt model definitions) pythonification ongoing! (theano-extension, models in python code, theano not hidden) and of course:
HOW EASY TO PROGRAM??
HOW EASY TO PROGRAM??
DEMO

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Computer vision, machine, and deep learning

  • 1. COMPUTER VISION, MACHINE, AND DEEP LEARNING WITH PYTHON Dr.Eng. Igi Ardiyanto
  • 2. PROFILE Igi Ardiyanto Field of Interest : Robotics Computer Vision Intelligent Transportation System Embedded System Parallel Computing Deep Learning More Information ?? http://te.ugm.ac.id/~igi
  • 3. What is Computer Vision? Computer Vision, Machine, and Deep Learning with Python
  • 4. COMPUTER VISION Make computers understand images and video What kind of scene? Where are the people? How far is the building? Where is Waldo? Like when human “sees” something …..
  • 5. VISION IS REALLY HARD  Vision is an amazing feat of natural intelligence  Visual cortex occupies about 50% of Macaque brain  More human brain devoted to vision than anything else Sik…sik…. Iki dolanan opo panganan, cuk?
  • 6. OPTICAL CHARACTER RECOGNITION (OCR) Digit recognition, AT&T labs http://www.research.att.com/~yann/ Technology to convert scanned docs to text • If you have a scanner, it probably came with OCR software License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
  • 7. FACE DETECTION  Many new digital cameras now detect faces  Canon, Sony, Fuji, …
  • 8. SMILE DETECTION Sony Cyber-shot® T70 Digital Still Camera
  • 9. What is Machine Learning? Computer Vision, Machine, and Deep Learning with Python
  • 10. MACHINE LEARNING  Machine learning is programming computers to optimize a performance criterion using example data or past experience.  There is no need to “learn” to calculate payroll  Learning is used when:  Human expertise does not exist (navigating on Mars),  Humans are unable to explain their expertise (speech recognition)  Solution changes in time (routing on a computer network)  Solution needs to be adapted to particular cases (user biometrics)
  • 11. COMPUTER VISION MEETS MACHINE LEARNING Dog Cat Raccoon Dog Train: Deploy: Training Labels Training Image Features Prediction Image Features Learned model
  • 12. IMAGE FEATURES ??  Color  Histograms  Shape  … Slide credit: L. Lazebnik
  • 13. VERY BRIEF TOUR OF SOME CLASSIFIERS  K-nearest neighbor  SVM  Boosted Decision Trees  Neural networks  Naïve Bayes  Bayesian network  Gaussian Logistic regression  Random Forests  RBMs  Etc.
  • 14. FACIAL ATTRACTIVENESS PREDICTION Yoona: Score 3.6 Yuri: Score 3.4 Tiffany: Score 3.8
  • 16. What is Deep Learning? Computer Vision, Machine, and Deep Learning with Python
  • 17. 1) A host of statistical machine learning techniques 2) Enables the automatic learning of feature hierarchies 3) Generally based on artificial neural networks DEEP LEARNING
  • 18.  English and Mandarin speech recognition  Transition from English to Mandarin made simpler by end-to-end DL  No feature engineering or Mandarin-specificsrequired  More accurate than humans  Error rate 3.7% vs. 4% for human tests http://arxiv.org/abs/1512.02595 END-TO-END DEEP LEARNING FOR ENGLISH AND MANDARIN SPEECH RECOGNITION BAIDU DEEP SPEECH 2
  • 19. FIRST COMPUTER PROGRAM TO BEAT A HUMAN GO PROFESSIONAL Training DNNs : 3 weeks, 340 million training steps on 50 GPUs Play : Asynchronousmulti-threadedsearch Simulations on CPUs, policy and value DNNs in parallel on GPUs Single machine: 40 search threads, 48 CPUs, and 8 GPUs Distributed version: 40 search threads, 1202 CPUs and 176 GPUs Outcome: Beat both European and World Go champions in best of 5 matches ALPHA-GO
  • 20. DEEP LEARNING EVERYWHERE INTERNET & CLOUD Image Classification Speech Recognition Language Translation Language Processing Sentiment Analysis Recommendation MEDIA & ENTERTAINMENT Video Captioning Video Search Real Time Translation AUTONOMOUS MACHINES Pedestrian Detection Lane Tracking Recognize Traffic Sign SECURITY & DEFENSE Face Detection Video Surveillance Satellite Imagery MEDICINE & BIOLOGY Cancer Cell Detection Diabetic Grading Drug Discovery
  • 21. So what’s the f*** there for Python? Computer Vision, Machine, and Deep Learning with Python
  • 22. WHAT IS PYTHON?  General purpose interpreted programming language  Widely used by scientists and programmers of all stripes  Supported by many 3rd-party libraries (currently 21,054 on the main python package website)  Free!
  • 23. WHY IS IT WELL-SUITED TO SCIENCE?  NumPy  Numerical library for python  Written in C, wrapped by python  Fast  Scipy  Built on top of NumPy (i.e. Also fast!)  Common maths, science, engineering routines  Matplotlib  Hugely flexible plotting library  Similar syntax to Matlab  Produces publication-quality output
  • 24. WHY IS PYTHON BETTER THAN WHAT I USE NOW?  It can do everything  Fast mathematical operations  Easy file manipulation  Format conversion  Plotting  Scripting  Command line  OK, not everything  Write thesis for you
  • 25. Python has a wide range of deep learning-related libraries available Low level High level (efficient gpu-powered math) (theano-wrapper, models in python code, abstracts theano away) (wrapper for theano, yaml, experiment-oriented) (computer-vision oriented DL framework, model-zoo, prototxt model definitions) pythonification ongoing! (theano-extension, models in python code, theano not hidden) and of course:
  • 26. HOW EASY TO PROGRAM??
  • 27. HOW EASY TO PROGRAM??
  • 28. DEMO