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Every image tells a story
• “A picture is worth a thousand words”
• Goal of computer vision: perceive the “story” behind the picture
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• Computepropertiesofthe world
– 3D shape
– Names of people or objects – What happened?
Montparnasse derailment
Can the computer match human perception?
• Yes and no (mainly no)
– computers can be better at
“easy” things
– humans are much better at “hard” things
• Buthugeprogresshas been made
– Accelerating in the last few years due to deep learning
– What is considered “hard” keeps changing
The goal of computer vision
Computers can gain some high-level understanding from digital images/videos
– wikipedia
The goal of computer vision
• Compute and understand the physical world
The goal of computer vision • Reconstruct 3D model from crowdsourcing
Internet Photos
Reconstructed 3D cameras and points
Dense 3D model
The goal of computer vision • Recognize objects and people
Terminator 2, 1991
The goal of computer vision
• Improve photos (“Computational Photography”)
Haze removal
Super-resolution (source: 2d3)
Inpainting / image completion (image credit: Hays and Efros)
Why study computer vision? • Billions of images/videos captured per day
• Huge number of useful applications
Optical character recognition (OCR) • If you have a scanner, it probably came with OCR software
Digit recognition, AT&T labs http://www.research.att.com/~yann/
License plate readers
http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
Automatic check processing
Source: S. detection
• Nearly all cameras detect faces in real time
Face Recognition
Face recognition
Who is she?
Source: S. -based biometrics
“How the Afghan Girl was Identified by Her Iris Patterns” Read the story
Source: S. recognition (in mobile phones)
Source: S. Identification
Merlin Bird ID (based on Cornell Tech technology!)
Plant Identification
is a research and educational initiative on plant biodiversity supported by Agropolis Foundation since 2009.
Marine Mammal Recognition
Under-water fish counting
Amazon Picking Challenge
http://www.robocup2016.org/en/events /amazon-picking-challenge/
Medical imaging
Healthcare
Gist – Chili fish head
Color moment – Braised pork FC7 – Steamed chicken feet
AlexNet – Chicken VGG – Chicken
Multi-task VGG – Chicken [chicken, chili, peanut]
Region-based Multi-task VGG
chicken: dice, stir-fry chili: dry
peanut: roasted
Virtual & Augmented Reality
6DoF head tracking Hand & body tracking
3D scene understanding
3D-360 video capture
Current state of the art
• Thisisaveryactiveresearcharea,andrapidly changing
– More apps in the next 5 years??
• Tolearnmoreaboutvisionapplicationsand
– maintains an excellent overview of vision companies
• http://www.cs.ubc.ca/spider/lowe/vision.html
Why is computer vision difficult?
Viewpoint variation
Illumination
Why is computer vision difficult?
Intra-class variation
Motion (Source: S. Lazebnik)
Background clutter
Machine Learning
Image processing Scene understanding Motion analysis Object recognition
Multimedia
Human Computer Interaction
Computational Photography
Medical Imaging
Neuroscience
Course information
• Prerequisites
– A good working knowledge of programming
– Data structure and algorithm
– Some math: linear algebra, vector calculus • Grading
– Assignments (30%) – Group project (20%) – Final exam (50%)
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