aula 01. introduction
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Computer Vision I
Introduction
Raul Queiroz Feitosa
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Content
What is CV?
CV Applications
Fundamental Steps
From DIP to CV
Course Content
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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
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Applications
Medical Image Analysis
Analysis of Remote Sensing Data
Biometrics
Security
Microscopy
Industrial Inspection
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Applications
Medical ImagesMicroscopyIndustrySecurity
Robot VisionBiometrics
Remote Sensing
much
more
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LVC Topics: Face Recognition
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Controle dePassaportes
Registro nico de Identidade Civil
RIC
Controle de AcessoAplicaes Criminais
LVC Topics: Face Recognition
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Suspect Behavior
Tracking
Recognition
Frontal View
LVC Topics: Face Recognition fromVideo
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LVC Topics: Medical Image Analysis
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LVC Topics: Remote Sensing
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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
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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
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Fundamental Steps
Image Enhancement: improves image quality
digitalimage
digitalimage
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
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Fundamental Steps
Segmentation: partitions the image intomeaningfull objects
segmentsdigital image
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
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Fundamental Steps
Post-Processing: support segmentation/description
segments segments
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
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Fundamental Steps
Description: converts the data into a form suitable
for processing
segments description
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
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xi=(xi1 xin)T
xp=(xp1 xpn)T
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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
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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
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From DIP to CV
Image Analysis/Understanding
From segmentation up to recognition.
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
Image Analysis
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From DIP to CV
Computer Vision
Tries to emulate human intelligence.
Emphasis on 3D analysis.
Acquisition Enhancement Segmentation Featureextraction
RecognitionPost-
processing
Computer Vision
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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
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Image Analysis
develops methods and algorithms able to extract
automatically useful information about the world.
ImageAnalysis
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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
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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)
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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.
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Next Topic
Digital
Image
Fundamentals