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 languageEnglish
Title of host publicationOptical Digital Image Processing,
EditorsG. Cristobal, P. Schelkens, H. Thienpont
PublisherBlackwell-Wiley
Pages643-666
Number of pages24
ISBN (Print)978-3-527-40956-3
Publication statusPublished - Apr 2011

    Research areas

  • Image Analysis, Image segmentation, Intermediate Level Vision, Markov Random Fields

ID: 2266954