Computer Vision Tutorialoptml.lehigh.edu/files/2019/10/OptML_CV_tutorial_1_compressed.pdfRelated...
Transcript of Computer Vision Tutorialoptml.lehigh.edu/files/2019/10/OptML_CV_tutorial_1_compressed.pdfRelated...
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Computer Vision Tutorial
basic introduction
Liyuan Cao Haidong Gu
Industrial and Systems Engineering
Lehigh University
OptML, Sep 4, 2019
Lehigh ISE OptML Sep 4, 2019 1 / 74
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Table of Contents
1 What is Computer Vision?
2 Related Fields of Computer Vision
3 Computer Vision Applications
4 Three-Level Paradigm
5 Types of CV Problems
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Table of Contents
1 What is Computer Vision?
2 Related Fields of Computer Vision
3 Computer Vision Applications
4 Three-Level Paradigm
5 Types of CV Problems
Lehigh ISE OptML Sep 4, 2019 3 / 74
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What is Computer Vision?
Objective of Computer Vision
The objective of computer vision is to make computers see and interpret the world likehumans and possibly even better than us.
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What is Computer Vision?
Human vision performs multiple visual tasks quite e↵ortlessly and e↵ectively.
How is visual information processed and understood in biological systems?
What is the nature of computation involved in visual tasks?
How might we build machines that can see?
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What is Computer Vision?
Figure: Ball Catch
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
Recreating human vision isn’t just a hard problem, it’s a set of them, each of which relieson the other.
Definition of Computer Vision
Computer Vision is concerned with the automatic extraction, analysis and understandingof useful information from a single image or a sequence of images.
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What is Computer Vision?
Computer Vision
theoretical and algorithmic basis toachieve automatic visual understanding
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What is Computer Vision?
Computer Vision
high-level understanding fromdigital images or videos
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What is Computer Vision?
From the biological science point of view,
Computer Vision
computer vision aims to come up withcomputational models for human
visual system
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What is Computer Vision?
From the engineering point of view,
Computer Vision
computer vision aims to build autonomoussystems to perform some of the tasks which
the human visual system can performand even surpass it in many cases.
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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What is Computer Vision?
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Table of Contents
1 What is Computer Vision?
2 Related Fields of Computer Vision
3 Computer Vision Applications
4 Three-Level Paradigm
5 Types of CV Problems
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Related Fields of Computer Vision
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What is Computer Vision?
Computer Vision
can be studied from a purely mathematical point of view
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Related Fields of Computer Vision
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Related Fields of Computer Vision
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Related Fields of Computer Vision
Computer vision systems rely on image processing techniques to pre-process the imagedata for robust high-level analysis.
Digital Image Processing
image compression
image restoration
image enhancement
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Related Fields of Computer Vision
The robust high-level analysis is the next major task in computer vision pipeline. It is thearea where neuroscience plays an important role. Specifically, the study of the biologicalvision system.
Neuroscience
(a) eye (b) neuron (c) brain structure
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Related Fields of Computer Vision
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Table of Contents
1 What is Computer Vision?
2 Related Fields of Computer Vision
3 Computer Vision Applications
4 Three-Level Paradigm
5 Types of CV Problems
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Computer Vision Applications
Computer vision is transitioning from a nascent
stage and is proving to be incredibly useful in
several application areas.
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Computer Vision Applications
Computer Vision for Security
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Computer Vision Applications
Figure: visual surveillance
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Computer Vision Applications
Figure: fingerprint-based identification and authentication
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Computer Vision Applications
Figure: iris identification and authentication
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Computer Vision Applications
Figure: face recognition
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Computer Vision Applications
Computer Vision in
Multimedia and Entertainment
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Computer Vision Applications
Figure: face recognition
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Computer Vision Applications
Figure: augmented reality game
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Computer Vision Applications
Computer Vision for Navigation
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Computer Vision Applications
Figure: robot navigation
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Computer Vision Applications
Figure: autonomous driving
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Computer Vision Applications
Computer Vision in Retail
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Computer Vision Applications
Figure: automated supermarket
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Computer Vision Applications
Figure: visual search on smartphone cameras
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Computer Vision Applications
Computer Vision in Industry
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Computer Vision Applications
Figure: real-time box analytics
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Computer Vision Applications
Figure: auto cycle counting
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Computer Vision Applications
Figure: inspect products
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Computer Vision Applications
Computer Vision for Social Causes
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Computer Vision Applications
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Table of Contents
1 What is Computer Vision?
2 Related Fields of Computer Vision
3 Computer Vision Applications
4 Three-Level Paradigm
5 Types of CV Problems
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Three-Level Paradigm
Computer vision conceptscan be broadly categorized as
low, mid and high level vision techniques
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Three-Level Paradigm
Low Level Vision
image processing techniques, feature detection andmatching and early segmentation
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Three-Level Paradigm
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Three-Level Paradigm
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Three-Level Paradigm
Mid Level Vision
image segmentation, things start to come togetherattributing meaning
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Three-Level Paradigm
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Three-Level Paradigm
High Level Vision
Visual recognition and scene understanding,algorithms which makes sense of the visualcontent and make computer vision live up
to the capabilities of human vision
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Three-Level Paradigm
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Three-Level Paradigm
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Three-Level Paradigm
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Three-Level Paradigm
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Three-Level Paradigm
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Three-Level Paradigm
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Table of Contents
1 What is Computer Vision?
2 Related Fields of Computer Vision
3 Computer Vision Applications
4 Three-Level Paradigm
5 Types of CV Problems
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Types of CV Problems
Recognition determining whether or not the image data contains some specific object,feature, or activity
Motion Analysis an image sequence is processed to produce an estimate of the velocityeither at each points in the image or in the 3D scene, or even of thecamera that produces the images
Scene Reconstruction computing a 3D model of the scene
Image Restoration The aim of image restoration is the removal of noise (sensor noise,motion blur, etc.) from images.
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Recognition: Object Recognition, Identification, Detection
Figure: Object Recognition: one or several pre-specified or learned objects or object classes can
be recognized, usually together with their 2D positions in the image or 3D poses in the scene
Figure: Identification: an individual instance of an object is recognized
Detection: the image data are scanned for a specific condition.Lehigh ISE OptML Sep 4, 2019 70 / 74
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Motion Analysis: Egomotion, Tracking, Optical flow
Figure: Egomotion: determining the 3D rigid motion (rotation and translation) of the camera
from an image sequence produced by the camera. (visual odometry)
Figure: Tracking: following the movements of a (usually) smaller set of interest points or objects
(e.g., vehicles, humans or other organisms) in the image sequence.
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Motion Analysis: Egomotion, Tracking, Optical flow
Figure: Optical Flow: to determine, for each point in the image, how that point is moving
relative to the image plane, i.e., its apparent motion. This motion is a result both of how the
corresponding 3D point is moving in the scene and how the camera is moving relative to the
scene.
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Scene Reconstruction
Figure: Scene Reconstruction: Given one or (typically) more images of a scene, or a video, scene
reconstruction aims at computing a 3D model of the scene.
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Image Restoration
(a) inpainting
Figure: Image Restoration
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