Transcript Introduction

Computer Vision I
Introduction
Raul Queiroz Feitosa
Content
What is CV?
 CV Applications
 Fundamental Steps
 From DIP to CV
 Course Content

5/2/2020
Introduction
2
What is Computer Vision
“Computer Vision is the science that develops
the theoretical and algorithmic basis by which
useful information about the world can be
automatically extracted and analyzed from an
observed image, image set, or image sequence
from computations made by a ... computer.” R. B.
Haralick, L.G. Shapiro

5/2/2020
Introduction
3
Applications







Medical Image Analysis
Analysis of Remote Sensing Data
Biometrics
Security
Microscopy
Industrial Inspection
…
5/2/2020
Introduction
4
Applications
much
more
Robot
Security
Vision
Remote
Sensing
Biometrics
Medical
Microscopy
Images
Industry
Introduction
5/2/2020
5
LVC Topics: Face Recognition
5/2/2020
Introduction
6
LVC Topics: Face Recognition
Registro Único de Identidade Civil
RIC
Controle de
Passaportes
Aplicações Criminais
5/2/2020
Introduction
Controle de Acesso
7
LVC Topics: Face Recognition from
Video
Frontal View
Tracking
Suspect Behavior
Recognition
5/2/2020
Introduction
8
LVC Topics: Medical Image Analysis
5/2/2020
Introduction
9
LVC Topics: Remote Sensing
5/2/2020
Introduction
10
LVC Applications: Remote Sensing
SAR R99B (SIPAM)
Illegal runways
5/2/2020
Introduction
Geometric features are used to
et from
al., 2009
distinguish landingAlves
lanes
other
targets in the forest.
11
Fundamental Steps
Image Acquisition: digitizes the electromagnetic
energy
gray level
physical
image
digital
image
(pixels)
Physical image
Acquisition
5/2/2020
Enhancement
digital image
Segmentation
Postprocessing
Introduction
Feature
extraction
(quem /
o que)
Recognition
12
Fundamental Steps
Image Enhancement: improves image quality
digital
image
Acquisition
5/2/2020
digital
image
Enhancement
Segmentation
Postprocessing
Introduction
Feature
extraction
Recognition
13
Fundamental Steps

Segmentation: partitions the image into
meaningfull objects
digital image
Acquisition
5/2/2020
Enhancement
segments
Segmentation
Postprocessing
Introduction
Feature
extraction
Recognition
14
Fundamental Steps
Post-Processing: support segmentation/description
segments
Acquisition
5/2/2020
Enhancement
Segmentation
segments
Postprocessing
Introduction
Feature
extraction
Recognition
15
Fundamental Steps
Description: converts the data into a form suitable
for processing
x1=(x11 … x1n)T
···
xi=(xi1 … xin)T
···
xp=(xp1 … xpn)T
segments
Acquisition
5/2/2020
Enhancement
Segmentation
Postprocessing
Introduction
description
Feature
extraction
Recognition
16
Fundamental Steps
Recognition: assigns a label to the image objects
x1=(x11 … x1n)T
paprika
···
···
xi=(xi1 … xin)T
pepper
description
Acquisition
5/2/2020
Enhancement
Segmentation
Postprocessing
Introduction
Feature
extraction
···
···
xp=(xp1 … xpn)T
cabbage
label
Recognition
17
From DIP to CV
Digital Image Processing


Input and output are images!
From image up to recognition!
DIP
DIP
Acquisition
5/2/2020
Enhancement
Segmentation
Postprocessing
Introduction
Feature
extraction
Recognition
18
From DIP to CV
Image Analysis/Understanding

From segmentation up to recognition.
Image Analysis
Acquisition
5/2/2020
Enhancement
Segmentation
Postprocessing
Introduction
Feature
extraction
Recognition
19
From DIP to CV
Computer Vision


Tries to emulate human intelligence.
Emphasis on 3D analysis.
Computer Vision
Acquisition
5/2/2020
Enhancement
Segmentation
Postprocessing
Introduction
Feature
extraction
Recognition
20
From DIP to CV
Process Levels



Low-level: input and outputs are images
Mid-level: image as input and attributes as output.
High-level: “making sense” of an ensemble of objects
High
Low
Acquisition
5/2/2020
Enhancement
Mid
Segmentation
Postprocessing
Introduction
Feature
extraction
Recognition
21
Image Analysis
develops methods and algorithms able to extract
automatically useful information about the world.
Image
Analysis
5/2/2020
Introduction
22
Computer Graphics
develps techniques for visualization and manipulation
of ideas that exist only conceptually or in form of
mathematical description, but not as concrete object.
Computer
Graphics
5/2/2020
Introduction
23
Course Content
Main:








Introduction
Digital Image Fundamentals
Image Enhancement in Spatial Domain
Image Enhancement in Frequency Domain
Morphological Image Processing
Segmentation
Representation and Description
Object Recognition
Appendices:


5/2/2020
Mathematical Foundation
Dimensionality Reduction (top)
Introduction
24
Bibliography
1.
2.
3.
4.
5.
R. G. Gonzalez, R. E. Woods, Digital Image Processing; Prentice Hall, 3rd Ed.,
2007
R. G. Gonzalez, R. E. Woods, Digital Image Processing; Prentice Hall, 2nd Ed.,
2002.
R. G. Gonzalez, R. E. Woods, S.L. Eddings, Digital Image Processing using
MATLAB; Prentice Hall, 2003.
M. Nixon, A. Aguado, Feature Extraction & Image Processing, Newnes, 2002.
R. O. Duda, Peter E. Hart, D. G. Stork, Pattern Classification, WileyInterscience; 2nd edition, 2000.
5/2/2020
Introduction
25
Next Topic
Digital
Image
Fundamentals
5/2/2020
Introduction
26