Download - Aula 01. Introduction
-
8/13/2019 Aula 01. Introduction
1/26
Computer Vision I
Introduction
Raul Queiroz Feitosa
-
8/13/2019 Aula 01. Introduction
2/26
11/19/2013 Introduction 2
Content
What is CV?
CV Applications
Fundamental Steps
From DIP to CV
Course Content
-
8/13/2019 Aula 01. Introduction
3/26
11/19/2013 Introduction 3
What is Computer Vision
Computer Vision is the science that develops
the theoretical and algor ithmic basis by which
useful information about the world can be
automatical ly extracted and analyzed from anobserved image, image set, or image sequence
from computations made by a ... computer. R. B.
Haralick, L.G. Shapiro
-
8/13/2019 Aula 01. Introduction
4/26
11/19/2013 Introduction 4
Applications
Medical Image Analysis
Analysis of Remote Sensing Data
Biometrics
Security
Microscopy
Industrial Inspection
-
8/13/2019 Aula 01. Introduction
5/26
11/19/2013Introduction
5
Applications
Medical ImagesMicroscopyIndustrySecurity
Robot VisionBiometrics
Remote Sensing
much
more
-
8/13/2019 Aula 01. Introduction
6/26
11/19/2013 Introduction 6
LVC Topics: Face Recognition
-
8/13/2019 Aula 01. Introduction
7/26
11/19/2013 Introduction 7
Controle dePassaportes
Registro nico de Identidade Civil
RIC
Controle de AcessoAplicaes Criminais
LVC Topics: Face Recognition
-
8/13/2019 Aula 01. Introduction
8/26
11/19/2013 Introduction 8
Suspect Behavior
Tracking
Recognition
Frontal View
LVC Topics: Face Recognition fromVideo
-
8/13/2019 Aula 01. Introduction
9/26
11/19/2013 Introduction 9
LVC Topics: Medical Image Analysis
-
8/13/2019 Aula 01. Introduction
10/26
LVC Topics: Remote Sensing
11/19/2013 Introduction 10
-
8/13/2019 Aula 01. Introduction
11/26
11/19/2013 Introduction 11
LVCApplications: Remote Sensing
Geometric features are used todistinguish landing lanes from othe
targets in the forest.
Illegal runways
SAR R99B (SIPAM)
Alves et al., 2009
-
8/13/2019 Aula 01. Introduction
12/26
11/19/2013 Introduction 12
Fundamental Steps
Image Acquisition: digitizes the electromagnetic
energy
(quem /o que)Physical image digital image
gray level
physicalimage
digitalimage
(pixels)
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
-
8/13/2019 Aula 01. Introduction
13/26
11/19/2013 Introduction 13
Fundamental Steps
Image Enhancement: improves image quality
digitalimage
digitalimage
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
-
8/13/2019 Aula 01. Introduction
14/26
11/19/2013 Introduction 14
Fundamental Steps
Segmentation: partitions the image intomeaningfull objects
segmentsdigital image
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
-
8/13/2019 Aula 01. Introduction
15/26
11/19/2013 Introduction 15
Fundamental Steps
Post-Processing: support segmentation/description
segments segments
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
-
8/13/2019 Aula 01. Introduction
16/26
11/19/2013 Introduction 16
Fundamental Steps
Description: converts the data into a form suitable
for processing
segments description
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
x1=(x11 x1n)T
xi=(xi1 xin)T
xp=(xp1 xpn)T
-
8/13/2019 Aula 01. Introduction
17/26
11/19/2013 Introduction 17
Fundamental Steps
Recognition: assigns a label to the image objects
description label
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
x1=(x11 x1n)T
xi=(xi1 xin)T
xp=(xp1 xpn)T
paprika
pepper
cabbage
-
8/13/2019 Aula 01. Introduction
18/26
11/19/2013 Introduction 18
From DIP to CV
Digital Image Processing
Input and output are images!
From image up to recognition!
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
DIP
DIP
-
8/13/2019 Aula 01. Introduction
19/26
11/19/2013 Introduction 19
From DIP to CV
Image Analysis/Understanding
From segmentation up to recognition.
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
Image Analysis
-
8/13/2019 Aula 01. Introduction
20/26
11/19/2013 Introduction 20
From DIP to CV
Computer Vision
Tries to emulate human intelligence.
Emphasis on 3D analysis.
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
Computer Vision
-
8/13/2019 Aula 01. Introduction
21/26
11/19/2013 Introduction 21
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
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
Low Mid
High
-
8/13/2019 Aula 01. Introduction
22/26
11/19/2013 Introduction 22
Image Analysis
develops methods and algorithms able to extract
automatically useful information about the world.
ImageAnalysis
-
8/13/2019 Aula 01. Introduction
23/26
11/19/2013 Introduction 23
Computer Graphics
develps techniques for visualization and manipulationof ideas that exist only conceptually or in form of
mathematical description, but not as concrete object.
Computer
Graphics
-
8/13/2019 Aula 01. Introduction
24/26
11/19/2013 Introduction 24
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: Mathematical Foundation
Dimensionality Reduction (top)
-
8/13/2019 Aula 01. Introduction
25/26
11/19/2013 Introduction 25
Bibliography
1. R. G. Gonzalez, R. E. Woods,Digital Image Processing; Prentice Hall, 3rd Ed.,2007
2. R. G. Gonzalez, R. E. Woods,Digital Image Processing; Prentice Hall, 2nd Ed.,2002.
3. R. G. Gonzalez, R. E. Woods, S.L. Eddings,Digital Image Processing usingMATLAB; Prentice Hall, 2003.
4. M. Nixon, A. Aguado,Feature Extraction & Image Processing, Newnes, 2002.
5. R. O. Duda, Peter E. Hart, D. G. Stork,Pattern Classification, Wiley-Interscience; 2nd edition, 2000.
-
8/13/2019 Aula 01. Introduction
26/26
11/19/2013 Introduction 26
Next Topic
Digital
Image
Fundamentals