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Machine Learning For Computer Vision
1
Machine Learning for Computer Vision
Ashutosh Upadhyay
Assistant Professor
School of Computer Science and Engineering
Galgotias University, India
Machine Learning For Computer Vision
2
Presentation Outline
What is computer Vision
Computer vision vs Human Vision
Need of Computer Vision
Challenges of Computer Vision
Applications of Computer Vision
Relation of Machine Learning with computer vision
Machine learning algorithms
Introduction of implementation using python
Machine Learning For Computer Vision
3
What is Computer Vision?
Make computers understand images and video
Like when human “sees” something and interpret
What kind of scene?
Where are the people?
How many Persons ?
How Many Cars?
How far is the building?
Following Social Distancing or Not?
Wearing mask or not?
Machine Learning For Computer Vision
4
It is a multidisciplinary field that could broadly be called a subfield of artificial
intelligence and machine learning, which may involve the use of specialized methods
and make use of general learning algorithms
What is Computer Vision?
Machine Learning For Computer Vision
5
Computer Vision vs Human Vision
Machine Learning For Computer Vision
6
Computer Vision vs Human Vision
Machine Learning For Computer Vision
7
Computer Vision vs Human Vision
Machine Learning For Computer Vision
8
Need of computer vision
To get the most out of image data, we need computers to “see” an image and
understand the content.
This is a trivial problem for a human
 A person can describe the content of a photograph they have seen once.
 A person can summarize a video that they have only seen once.
 A person can recognize a face that they have only seen once before.
 We require at least the same capabilities from computers in order to unlock our
images and videos.
Machine Learning For Computer Vision
9
Challenge of Computer Vision
The goal of computer vision is to extract useful information from images.
 Dynamic shape of object
 Changes in illumination
 Orientation of the objects
 Partial occlusion
 Shadowing effect
Machine Learning For Computer Vision
10
Application of Computer Vision
 Optical character recognition (OCR)
 Machine inspection
 Retail (automated checkouts Amazon Store)
 Medical imaging
 Automotive safety
 Motion capture
 Surveillance
 Fingerprint recognition and biometrics
Machine Learning For Computer Vision
11
 Object Classification
 Object Identification
 Object Verification
 Object Detection
 Object Landmark Detection
 Object Segmentation
 Object Recognition
Application of Computer Vision
Machine Learning For Computer Vision
12
MACHINE LEARNING
Machine learning is programming computers to optimize a performance criterion using
example data or past experience.
Learning is used when:
 Human expertise does not exist
 Humans are unable to explain their expertise
Machine Learning For Computer Vision
13
Training Models
Machine Learning For Computer Vision
14
Computer Vision Meets Machine Learning
Dog
Cat
Dog
Train:
Deploy:
Training
Labels
Training
Image
Features
Prediction
Image
Features
Learned
model
Machine Learning For Computer Vision
15
Image Features ??
Slide credit: L. Lazebnik
 Colour
 Edge
 Histogram
 Shape
 LBP
 HAAR
 HOG
 Region etc.
Machine Learning For Computer Vision
16
Brief Tour of Some Classifiers
 K-nearest neighbor
 SVM
 Decision Trees
 Neural networks
 Naïve Bayes
 Bayesian network
 Logistic regression
 Random Forests
Etc.
Machine Learning For Computer Vision
17
 Deep learning is an artificial intelligence
function that imitates the workings of the
human brain in processing data and creating
patterns for use in decision making.
 Enables the automatic learning of feature
 Generally based on artificial neural
networks
Deep Learning
Machine Learning For Computer Vision
18
Deep Learning Everywhere
MEDICINE &BIOLOGY
Cancer Cell Detection Diabetic Grading Drug Discovery
INTERNET & CLOUD
Image Classification Speech Recognition Language
Translation Language Processing Sentiment Analysis
Recommendation
Machine Learning For Computer Vision
19
SECURITY &
DEFENSE
Face Detection Video Surveillance Satellite Imagery
AUTONOMOUS MACHINES
Pedestrian Detection Lane Tracking
Recognize Traffic Sign
Machine Learning For Computer Vision
20
Why Python Is Well-suited To Data Science?
NumPy
 Numerical library for python
 Fast
Scipy
 Common maths, science, engineering routines
Matplotlib
 Hugely flexible plotting library
 Similar syntax to Matlab
 Produces publication-quality output
Machine Learning For Computer Vision
21
Thank You

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ML for Computer Vision: Applications and Algorithms

  • 1. Machine Learning For Computer Vision 1 Machine Learning for Computer Vision Ashutosh Upadhyay Assistant Professor School of Computer Science and Engineering Galgotias University, India
  • 2. Machine Learning For Computer Vision 2 Presentation Outline What is computer Vision Computer vision vs Human Vision Need of Computer Vision Challenges of Computer Vision Applications of Computer Vision Relation of Machine Learning with computer vision Machine learning algorithms Introduction of implementation using python
  • 3. Machine Learning For Computer Vision 3 What is Computer Vision? Make computers understand images and video Like when human “sees” something and interpret What kind of scene? Where are the people? How many Persons ? How Many Cars? How far is the building? Following Social Distancing or Not? Wearing mask or not?
  • 4. Machine Learning For Computer Vision 4 It is a multidisciplinary field that could broadly be called a subfield of artificial intelligence and machine learning, which may involve the use of specialized methods and make use of general learning algorithms What is Computer Vision?
  • 5. Machine Learning For Computer Vision 5 Computer Vision vs Human Vision
  • 6. Machine Learning For Computer Vision 6 Computer Vision vs Human Vision
  • 7. Machine Learning For Computer Vision 7 Computer Vision vs Human Vision
  • 8. Machine Learning For Computer Vision 8 Need of computer vision To get the most out of image data, we need computers to “see” an image and understand the content. This is a trivial problem for a human  A person can describe the content of a photograph they have seen once.  A person can summarize a video that they have only seen once.  A person can recognize a face that they have only seen once before.  We require at least the same capabilities from computers in order to unlock our images and videos.
  • 9. Machine Learning For Computer Vision 9 Challenge of Computer Vision The goal of computer vision is to extract useful information from images.  Dynamic shape of object  Changes in illumination  Orientation of the objects  Partial occlusion  Shadowing effect
  • 10. Machine Learning For Computer Vision 10 Application of Computer Vision  Optical character recognition (OCR)  Machine inspection  Retail (automated checkouts Amazon Store)  Medical imaging  Automotive safety  Motion capture  Surveillance  Fingerprint recognition and biometrics
  • 11. Machine Learning For Computer Vision 11  Object Classification  Object Identification  Object Verification  Object Detection  Object Landmark Detection  Object Segmentation  Object Recognition Application of Computer Vision
  • 12. Machine Learning For Computer Vision 12 MACHINE LEARNING Machine learning is programming computers to optimize a performance criterion using example data or past experience. Learning is used when:  Human expertise does not exist  Humans are unable to explain their expertise
  • 13. Machine Learning For Computer Vision 13 Training Models
  • 14. Machine Learning For Computer Vision 14 Computer Vision Meets Machine Learning Dog Cat Dog Train: Deploy: Training Labels Training Image Features Prediction Image Features Learned model
  • 15. Machine Learning For Computer Vision 15 Image Features ?? Slide credit: L. Lazebnik  Colour  Edge  Histogram  Shape  LBP  HAAR  HOG  Region etc.
  • 16. Machine Learning For Computer Vision 16 Brief Tour of Some Classifiers  K-nearest neighbor  SVM  Decision Trees  Neural networks  Naïve Bayes  Bayesian network  Logistic regression  Random Forests Etc.
  • 17. Machine Learning For Computer Vision 17  Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making.  Enables the automatic learning of feature  Generally based on artificial neural networks Deep Learning
  • 18. Machine Learning For Computer Vision 18 Deep Learning Everywhere MEDICINE &BIOLOGY Cancer Cell Detection Diabetic Grading Drug Discovery INTERNET & CLOUD Image Classification Speech Recognition Language Translation Language Processing Sentiment Analysis Recommendation
  • 19. Machine Learning For Computer Vision 19 SECURITY & DEFENSE Face Detection Video Surveillance Satellite Imagery AUTONOMOUS MACHINES Pedestrian Detection Lane Tracking Recognize Traffic Sign
  • 20. Machine Learning For Computer Vision 20 Why Python Is Well-suited To Data Science? NumPy  Numerical library for python  Fast Scipy  Common maths, science, engineering routines Matplotlib  Hugely flexible plotting library  Similar syntax to Matlab  Produces publication-quality output
  • 21. Machine Learning For Computer Vision 21 Thank You