Deep Learning for Computer Vision
David Willingham – Senior Application Engineer
[email protected]
© 2016 The MathWorks, Inc. 1
Learning Game
Question – At what age does a person recognise: – Car or Plane – Car or SUV – Toyota or Mazda
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What dog breeds are these?
Source 3
Demo : Live Object Recognition with Webcam
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Computer Vision Applications
Pedestrian and traffic sign detection Landmark identification Scene recognition Medical diagnosis and drug discovery Public Safety / Surveillance Automotive Robotics
and many more… 5
Deep Learning investment is rising
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What is Deep Learning ? Deep learning performs end-end learning by learning features, representations and tasks directly from images, text and sound Traditional Machine Learning
Manual Feature Extraction
Classification Machine Learning
Car Truck
Bicycle Deep Learning approach
Convolutional Neural Network (CNN) Learned features 𝟗𝟓% End-to-end learning … 𝟑% Feature learning + Classification 𝟐%
Car Truck
Bicycle
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What is Feature Extraction ?
SURF Bag of Words
HOG
Image Pixels
Feature Extraction • Representations often invariant to changes in scale, rotation, illumination • More compact than storing pixel data • Feature selection based on nature of problem
Sparse
Dense
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Why is Deep Learning so Popular ? Year
Results: Achieved substantially better results on ImageNet large scale recognition challenge – 95% + accuracy on ImageNet 1000 class challenge
Computing Power: GPU’s and advances to processor technologies have enabled us to train networks on massive sets of data.
Data: Availability of storage and access to large sets of labeled data – E.g. ImageNet , PASCAL VoC , Kaggle
Pre-2012 (traditional computer vision and machine learning techniques)
Error Rate > 25%
2012 (Deep Learning )
~ 15%
2015 ( Deep Learning)