Key members: Péter Horváth [1] Zoltán Kató [2] Founded by: Hungarian Science and Technology Foundation (TET), Hungarian-French Cooperation (Balaton) [3] Hungarian Scientific Research Fund (OTKA) [4] European Union FP5 project IMAVIS PhD Scholarship of the Doctoral School in Mathematics and Computer Science of the University of Szeged [5] PhD Scholarship of the French Government Partners: Ariana...

Key members: Péter Horváth [1] Zoltán Kató [2] Founded by: Hungarian Science and Technology Foundation (TET), Hungarian-French Cooperation (Balaton) [3] Hungarian Scientific Research Fund (OTKA) [4] European Union FP5 project IMAVIS PhD Scholarship of the Doctoral School in Mathematics and Computer Science of the University of Szeged [5] PhD Scholarship of the French Government Partners: Ariana...

In the paper we propose a new deformable shape model that is based on simplified skeleton graph. Such shape model allows to account for different shape variations and to introduce global constraints like known orientation or scale of the object. We combine the model with low-level image segmentation techniques based on Markov random fields and derive an approximate algorithm for the minimizatio...

“Shape” and “appearance”, the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation. The recently proposed Sparse Shape Composition (SSC) 49,51 opens a new avenue for shape prior modeling. Inst...

In this paper we propose a new variational framework for image segmentation that incorporates the information of expected shape and a few points on the boundary into geodesic active contours. The energy functional in this model consists of three terms. One measures high image gradients, the other two measure the disparity in shape between the interface and prior, and the distance from the prior...

We propose a unified framework for boundary finding, where a Bayesian formulation, based on prior knowledge and the edge information of the input image (likelihood), is employed. The prior knowledge in our framework is based on principal component analysis of four different covariance matrices corresponding to independence, smoothness, statistical shape and combined models, respectively. Indeed...

Key members: Péter Horváth [1] Zoltán Kató [2] Founded by: Hungarian Science and Technology Foundation (TET), Hungarian-French Cooperation (Balaton) [3] Hungarian Scientific Research Fund (OTKA) [4] European Union FP5 project IMAVIS PhD Scholarship of the Doctoral School in Mathematics and Computer Science of the University of Szeged [5] PhD Scholarship of the French Government Partners: Ariana...

Key members: Péter Horváth [1] Zoltán Kató [2] Founded by: Hungarian Science and Technology Foundation (TET), Hungarian-French Cooperation (Balaton) [3] Hungarian Scientific Research Fund (OTKA) [4] European Union FP5 project IMAVIS PhD Scholarship of the Doctoral School in Mathematics and Computer Science of the University of Szeged [5] PhD Scholarship of the French Government Partners: Ariana...

Key members: Péter Horváth [1] Zoltán Kató [2] Founded by: Hungarian Science and Technology Foundation (TET), Hungarian-French Cooperation (Balaton) [3] Hungarian Scientific Research Fund (OTKA) [4] European Union FP5 project IMAVIS PhD Scholarship of the Doctoral School in Mathematics and Computer Science of the University of Szeged [5] PhD Scholarship of the French Government Partners: Ariana...

This report surveys current literature related to statistical shape analysis. There are two paradigms: (1) Models that have no prior knowledge about the shape. They evolve boundary curves in time while maintaining predefined smoothness constraints. (2) Models that have prior knowledge about a fixed shape. They use this knowledge to find object boundaries using the covariance matrix and the aver...