We propose a novel automatic salient object segmentation algorithm which integrates both bottom-up salient stimuli and object-level shape prior, i.e., a salient object has a well-defined closed boundary. Our approach is formalized as iterative energy minimization framework, leading to binary segmentation of the salient object. Such energy minimization is initialized with saliency map which is c...

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 this note we present a coupled optimization model for boundary determination. One part of the model incorporates a prior shape into a geometric active contour model with a fixed parameter. The second part determines the ‘best’ parameter used in the first part by maximizing the mutual information of the image geometry between the prior and an aligned novel image over all the alignments, that ...

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...

Incorporating shape priors in image segmentation has become a key problem in computer vision. Most existing work is limited to a linearized shape space with small deformation modes around a mean shape. These approaches are relevant only when the learning set is composed of very similar shapes. Also, there is no guarantee on the visual quality of the resulting shapes. In this paper, we introduce...

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...

We present a fully automatic segmentation method to extract media-adventitia border in IVUS images with shape prior information. Segmentation of IVUS has shown to be an intricate process due to relatively low contrast and various forms of interferences and artefacts caused by, for instance, calcification and acoustic shadow. We incorporate shape prior with an automatic graph cut technique to pr...

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing p...

Correspondence of features between two or more images obtained from different views of the same object is still a challenging problem in vision. In many applications, these two views are part of a larger video sequence. The aim of this paper is to show that the original video data contains enough information (referred to as prior information) to design a robust correspondence algorithm. A doubl...

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...