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 graph cuts-based image segmentation technique that incorporates an elliptical shape prior. Inclusion of this shape constraint restricts the solution space of the segmentation result, increasing robustness to misleading information that results from noise, weak boundaries, and clutter. We argue that combining a shape prior with a graph cuts method suggests an iterative approach that...

We propose a novel kidney segmentation approach based on the graph cuts technique. The proposed approach depends on both image appearance and shape information. Shape information is gathered from a set of training shapes. Then we estimate the shape variations using a new distance probabilistic model which approximates the marginal densities of the kidney and its background in the variability re...

In this paper, we formulate the shape localization problem in the Bayesian framework. In the learning stage, we propose the Constrained RankBoost approach to model the likelihood of local features associated with the key points of an object, like face, while preserve the prior ranking order between the ground truth position of a key point and its neighbors; in the inferring stage, a simple effi...

Automated cell tracking in populations is very crucial for studying dynamic cell cycle behaviors. However, a high accuracy of each step is essential to avoid error propagation. In this paper, we propose an integrated three-component system to tackle this problem. We first model the temporal dynamics of shape change using an autoregressive model, which is used for estimating the shape and the lo...

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 Gro...

In recent years, segmentation with graph cuts is increasingly used for a variety of applications, such as photo/video editing, medical image processing, etc. One of the most common applications of graph cut segmentation is extracting an object of interest from its background. If there is any knowledge about the object shape (i.e. a shape prior), incorporating this knowledge helps to achieve a m...

Level set methods are effective for image segmentation problems. However, the methods suffer from limitations such as slow convergence and leaking problems. As such, over the past two decades, the original level set method has been evolved in many directions, including integration of prior shape models into the segmentation framework. In this paper, we introduce a new prior shape model for leve...

The Geometric Active Contour (GAC) framework, which utilizes image information, has proven to be quite valuable for performing segmentation. However, the use of image information alone often leads to poor segmentation results in the presence of noise, clutter or occlusion. The introduction of shapes priors in the contour evolution proved to be an effective way to circumvent this issue. Recently...

Over a number of years the authors have developed various methods for real-time tracking objects or tracking mobile cameras, both of which are essential precursors for many applications, not least augmented reality tasks. Though many algorithms for visual tracking are content with simply reporting a 2D x-y position in the image, a richer set of applications is available when the full pose of th...