Computer Vision IIntroduction

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 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

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Applications

Medical Image Analysis Analysis of Remote Sensing Data Biometrics Security Microscopy Industrial Inspection …

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Applications

Medical ImagesMicroscopy IndustrySecurityRobot Vision

BiometricsRemote Sensing

much more

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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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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

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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)

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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 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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Next Topic

Digital

Image

Fundamentals