introduction & motivation - unibas.ch · 2016-02-22 · introduction to signal and image...

Introduction &Motivation

Introduction toSignal and Image

Processing

Prof. Dr. Philippe Cattin

MIAC, University of Basel

February 23rd, 2016

February 23rd, 2016Introduction to Signal and Image Processing

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Ph. Cattin: Introduction & Motivation

Contents

Abstract

1 Motivation

Motivation

Application Scenarios

Challenges

Aim of this Lecture

Imaging Examples (1)


Definitions/Typology

Typical Application Areas of Computer Vision

Principle of a Computer Vision System

This is, however, not always easy!

This is, however, not always easy! (2)

Computer Vision vs. Computer Graphics


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(2)


Abstract

In the Introduction a brief overview of Computer Visionand its possible applications is given. Additionally someimportant definitions and explanations are given.

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Motivation


(4)Motivation

The human brain is unparallelled in 2D image

analysis and image understanding

Half our brain is devoted to processing and

interpretation of visual data

Limitations of the human visual system

Quantification

Reconstruction of 3D scenes from images

High dimensional image spaces

Practical applications

Quantitative measurements

Automatic analysis of images

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Motivation

(5)


Application Scenarios

Inspection

Positioning, registration, metrology

Assembly, navigation, and visual

control

OCR, document processing,

multimedia retrieval

Scene reconstruction, visualisation,

and editing

Image compression

Image enhancement and

restoration

Pattern, object, and event

recognition

Human motion/gesture/face

recognition and interpretation

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Motivation

(6)


Challenges

There are still many visual tasks humans can easily do,but that are beyond the reach of computer visionsystems:

Cue Integration

Today: Perfection of cues such as edges, motion,

depth, texture

Future: Integration of multiple cues

Dynamic 3D

Today: Excellent 3D capturing methods available

Future: But the world is 4D (3D+time)

Recognition of object categories

Today: Vision system can recognise individual

objects under varying circumstances

Future: General categorisation problem unsolved

(exceptions: face/OCR)

Shape and scene representations

How to model objects and scenes? For which objects does texture

suffice and where do we need more geometric detail (3D

information)?

Grouping and segmentation

Check-and-Egg Problem: First, segment the different objects,

then recognise the objects. However, how to segment the objects

without exact knowledge about them?

More self-learning and self-diagnosis

Most vision systems don't adapt and don't learn once in

operation. Future systems have to work in less structured

environments. Vision system will have to understand their own

limitations and report them back.

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Motivation

(7)


Aim of this Lecture

Signal processing background

Present a basic set of methods commonly used in CV

How are they used?

How can I combine them?

How can I determine their performance?

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Motivation

(8)



Human Visual System

Scene: Real 3D objects

with colour/brightness

Image: Two 2D images

with colour and

brightness

Satellites

Scene: Earth, 2D/3D,...

Image: 2D Images with

different frequency bands,

3D

Camera Images (mimicing

human vision)

Scene: Real 3D objects

with colour/brightness

Image: 2D Image with

brightness, colour/BW

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Motivation

(9)



Computed Tomography

Scene: Body, 3D,

Absorption of x-ray

Image: 3D matrix with

absorption coefficients

Magnet Resonance Imaging

Scene: Body, 3D, tissue

properties

Image: 3D matrix with

tissue properties

Depth Images

Scene: 3D Objects

Image: 2D Image with

depth information

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Motivation

(10)


Definitions/Typology

Computer Vision

Image Processing

(image → image)

Image Analysis (image

→ abstract description)

Image Understanding

(image → object

recognition)

Computer Graphics

Image Synthesis

(abstract

description →

image)

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Motivation

(11)


Typical ApplicationAreas of Computer Vision

Image Acquisition

Preprocessing: denoising, remove camera

distortions,...

Storing

Compression

Context based retrieval

Visualisation

Synthetic 3D views

Highlight/segment special regions

Image Analysis

Image Fusion (pixel positions represent the same

spatial region)

Combination of images showing different spectral

bands (Mutual Information)

Combination of images captured with different

imaging modalities (MR/CT)

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Motivation

(12)


Principle of a ComputerVision System

Brightness/Colour ←→ Structure

The Aim

We want to extract geometric, topologic, and

semantic object information as well as relations

between objects

Measure structures, identify objects such as

streets, buildings, cars,...

Important Issues

Accuracy, robustness, automation, complexity,

flexibility,...

Means

Generic object properties

Homogeneous properties within the objects

Strong changes at borders between objects

A priori knowledge about the scene

Include experience and expert knowledge:

mathematically difficult

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Motivation

(13)


This is, however, notalways easy!

M.C. Escher(1898-1972)

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Motivation

(14)


This is, however, notalways easy! (2)

Fig 1.1: M.C. Escher (1898-1972)

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Motivation

(15)


Computer Vision vs.Computer Graphics

Digital Image

ComputerVision

→ Building

floor plan

↓

ComputerGraphics

← Abstract

description

of buildings

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Fig 1.2: Flight through Pompeii (IT)

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introduction & motivation - unibas.ch · 2016-02-22 · introduction to signal and image...

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