Computer Vision I Introduction Raul Queiroz Feitosa.
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Transcript of Computer Vision I Introduction Raul Queiroz Feitosa.
Computer Vision IIntroduction
Raul Queiroz Feitosa
04/18/23 Introduction 2
Content
What is CV? CV Applications Fundamental Steps From DIP to CV Course Content
04/18/23 Introduction 3
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
04/18/23 Introduction 4
Applications
Medical Image Analysis Analysis of Remote Sensing Data Biometrics Security Microscopy Industrial Inspection …
04/18/23Introduction
5
Applications
Medical ImagesMicroscopy IndustrySecurityRobot Vision
BiometricsRemote Sensing
much more
04/18/23 Introduction 6
LVC Topics: Face Recognition
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Controle de Passaportes
Registro Único de Identidade Civil RIC
Controle de AcessoAplicações Criminais
LVC Topics: Face Recognition
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Suspect Behavior
Tracking
Recognition
Frontal View
LVC Topics: Face Recognition from Video
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LVC Topics: Medical Image Analysis
LVC Topics: Remote Sensing
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04/18/23 Introduction 11
LVC Applications: Remote Sensing
Geometric features are used to distinguish landing lanes from other 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
physical image
digital image
(pixels)
Acquisition Enhancement Segmentation Feature extraction
RecognitionPost-
processing
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Fundamental Steps
Image Enhancement: improves image quality
digital image
digital image
Acquisition Enhancement Segmentation Feature extraction
RecognitionPost-
processing
04/18/23 Introduction 14
Fundamental Steps Segmentation: partitions the image into
meaningfull objects
segmentsdigital image
Acquisition Enhancement Segmentation Feature extraction
RecognitionPost-
processing
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Fundamental Steps
Post-Processing: support segmentation/description
segments segments
Acquisition Enhancement Segmentation Feature extraction
RecognitionPost-
processing
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Fundamental Steps
Description: converts the data into a form suitable for processing
segments description
Acquisition Enhancement Segmentation Feature extraction
RecognitionPost-
processing
x1=(x11 … x1n)T
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 Feature extraction
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 Feature extraction
RecognitionPost-
processing
DIP
DIP
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From DIP to CV
Image Analysis/Understanding From segmentation up to recognition.
Acquisition Enhancement Segmentation Feature extraction
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 Feature extraction
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 Feature extraction
RecognitionPost-
processing
Low Mid
High
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Image Analysis
develops methods and algorithms able to extract automatically useful information about the world.
Image Analysis
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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
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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)
04/18/23 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 using MATLAB; 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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