Image Analysis: Intermediate-Level Vision

Jan Cornelis; Aneta Markova; Rudi Deklerck

Image Analysis: Intermediate-Level Vision

Jan Cornelis, Aneta Markova, Rudi Deklerck

Electronics and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

Abstract

1 Image Analysis: Intermediate Level Vision 3
Jan Cornelis, Aneta Markova and Rudi Deklerck
1.1 Introduction: Segmentation defined in the context of intermediate level vision
3
1.2 Pixel and Regionbased
segmentation 5
1.2.1 Examples of supervised approaches 6
1.2.2 Examples of unsupervised approaches 7
1.2.3 Improving the connectivity of the classification results 10
1.3 Edgebased
Segmentation 11
1.4 Deformable models 15
1.4.1 Mathematical Formulation (Continuous case) 16
1.4.2 Mathematical Formulation (The discrete case) 18
1.4.3 Applications of active contours 20
1.4.4 The behaviour of snakes 21
1.5 Modelbased
Segmentation 24
1.5.1 Statistical Labeling 24
1.5.2 Bayesian Decision Theory 24
1.5.3 Graphs and Markov Random Fields defined on a graph 25
1.5.4 Cliques 26
1.5.5 Models for the priors 26
1.5.6 Labeling in a Bayesian framework based onMarkov Random fieldmodelling 27
1.5.7 Examples 27

Original language	English
Title of host publication	Optical Digital Image Processing,
Editors	G. Cristobal, P. Schelkens, H. Thienpont
Publisher	Blackwell-Wiley
Pages	643-666
Number of pages	24
ISBN (Print)	978-3-527-40956-3
Publication status	Published - Apr 2011

Bibliographical note

G. Cristobal, P. Schelkens, H. Thienpont

Keywords

Image Analysis
Image segmentation
Intermediate Level Vision
Markov Random Fields

1 Active
2 Finished

IBBT32: ISBO : oa Terra, NEXTgen HD, SenseMAP, Schrecs
Munteanu, A., Verhelst, W., Deklerck, R., Barbarien, J., Dooms, A., Jansen, B. & Schelkens, P.
1/09/09 → …
Project: Applied
HOA14: Quantitative imaging with dynamic contrast CT-applications in tumor models in small animals.
De Mey, J., Nyssen, E., Bossuyt, A. & Deklerck, R.
1/01/07 → 31/12/10
Project: Fundamental
FWOAL390: Quantative measure of cancer treatment response using automated segmentation on volumetric and dynamic contrast enhanced CT-images in combination with pet.
Deklerck, R., Verellen, D., Storme, G., Nyssen, E. & Everaert, H.
1/01/06 → 31/12/09
Project: Fundamental

Cite this

@inbook{bb406cd4d35a498ca66e366172d351ec,

title = "Image Analysis: Intermediate-Level Vision",

abstract = "1 Image Analysis: Intermediate Level Vision 3 Jan Cornelis, Aneta Markova and Rudi Deklerck 1.1 Introduction: Segmentation defined in the context of intermediate level vision 3 1.2 Pixel and Regionbased segmentation 5 1.2.1 Examples of supervised approaches 6 1.2.2 Examples of unsupervised approaches 7 1.2.3 Improving the connectivity of the classification results 10 1.3 Edgebased Segmentation 11 1.4 Deformable models 15 1.4.1 Mathematical Formulation (Continuous case) 16 1.4.2 Mathematical Formulation (The discrete case) 18 1.4.3 Applications of active contours 20 1.4.4 The behaviour of snakes 21 1.5 Modelbased Segmentation 24 1.5.1 Statistical Labeling 24 1.5.2 Bayesian Decision Theory 24 1.5.3 Graphs and Markov Random Fields defined on a graph 25 1.5.4 Cliques 26 1.5.5 Models for the priors 26 1.5.6 Labeling in a Bayesian framework based onMarkov Random fieldmodelling 27 1.5.7 Examples 27",

keywords = "Image Analysis, Image segmentation, Intermediate Level Vision, Markov Random Fields",

author = "Jan Cornelis and Aneta Markova and Rudi Deklerck",

note = "G. Cristobal, P. Schelkens, H. Thienpont",

year = "2011",

month = apr,

language = "English",

isbn = "978-3-527-40956-3",

pages = "643--666",

editor = "G. Cristobal and P. Schelkens and H. Thienpont",

booktitle = "Optical Digital Image Processing,",

publisher = "Blackwell-Wiley",

}

TY - CHAP

T1 - Image Analysis: Intermediate-Level Vision

AU - Cornelis, Jan

AU - Markova, Aneta

AU - Deklerck, Rudi

N1 - G. Cristobal, P. Schelkens, H. Thienpont

PY - 2011/4

Y1 - 2011/4

N2 - 1 Image Analysis: Intermediate Level Vision 3 Jan Cornelis, Aneta Markova and Rudi Deklerck 1.1 Introduction: Segmentation defined in the context of intermediate level vision 3 1.2 Pixel and Regionbased segmentation 5 1.2.1 Examples of supervised approaches 6 1.2.2 Examples of unsupervised approaches 7 1.2.3 Improving the connectivity of the classification results 10 1.3 Edgebased Segmentation 11 1.4 Deformable models 15 1.4.1 Mathematical Formulation (Continuous case) 16 1.4.2 Mathematical Formulation (The discrete case) 18 1.4.3 Applications of active contours 20 1.4.4 The behaviour of snakes 21 1.5 Modelbased Segmentation 24 1.5.1 Statistical Labeling 24 1.5.2 Bayesian Decision Theory 24 1.5.3 Graphs and Markov Random Fields defined on a graph 25 1.5.4 Cliques 26 1.5.5 Models for the priors 26 1.5.6 Labeling in a Bayesian framework based onMarkov Random fieldmodelling 27 1.5.7 Examples 27

AB - 1 Image Analysis: Intermediate Level Vision 3 Jan Cornelis, Aneta Markova and Rudi Deklerck 1.1 Introduction: Segmentation defined in the context of intermediate level vision 3 1.2 Pixel and Regionbased segmentation 5 1.2.1 Examples of supervised approaches 6 1.2.2 Examples of unsupervised approaches 7 1.2.3 Improving the connectivity of the classification results 10 1.3 Edgebased Segmentation 11 1.4 Deformable models 15 1.4.1 Mathematical Formulation (Continuous case) 16 1.4.2 Mathematical Formulation (The discrete case) 18 1.4.3 Applications of active contours 20 1.4.4 The behaviour of snakes 21 1.5 Modelbased Segmentation 24 1.5.1 Statistical Labeling 24 1.5.2 Bayesian Decision Theory 24 1.5.3 Graphs and Markov Random Fields defined on a graph 25 1.5.4 Cliques 26 1.5.5 Models for the priors 26 1.5.6 Labeling in a Bayesian framework based onMarkov Random fieldmodelling 27 1.5.7 Examples 27

KW - Image Analysis

KW - Image segmentation

KW - Intermediate Level Vision

KW - Markov Random Fields

M3 - Chapter

SN - 978-3-527-40956-3

SP - 643

EP - 666

BT - Optical Digital Image Processing,

A2 - Cristobal, G.

A2 - Schelkens, P.

A2 - Thienpont, H.

PB - Blackwell-Wiley

ER -

Image Analysis: Intermediate-Level Vision

Abstract

Bibliographical note

Keywords

Fingerprint

Projects

IBBT32: ISBO : oa Terra, NEXTgen HD, SenseMAP, Schrecs

HOA14: Quantitative imaging with dynamic contrast CT-applications in tumor models in small animals.

FWOAL390: Quantative measure of cancer treatment response using automated segmentation on volumetric and dynamic contrast enhanced CT-images in combination with pet.

Cite this