Costas Panagiotakis

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Over the last years major eorts have b e e n m a d e t o d e v elop methods for extracting information from audiovisual media, in order that they may be stored and retrieved in databases automatically, based on their content. In this work we deal with the characterization of an audio signal, which m a y be part of a larger audiovisual system or may be(More)
We present a model-based, top-down solution to the problem of tracking the 3D position, orientation and full articulation of the human body from markerless visual observations obtained by two synchronized RGBD cameras. Inspired by recent advances to the problem of model-based hand tracking Oikonomidis et al. (Efficient Model-based 3D Tracking of Hand(More)
—Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub-)trajectories in the MOD. In order to find the most representative(More)
We propose a general purpose image segmentation framework, which involves feature extraction and classification in feature space, followed by flooding and merging in spatial domain. Region growing is based on the computed local measurements and distances from the distribution of features describing the different classes. Using the properties of the label(More)
We present a method to solve the human silhouette tracking problem using 18 major human points. We used: a simple 2D model for the human silhouette, a linear prediction technique for initializing major points search, geometry anthropometric constraints for determining the search area and color measures for matching human body parts. In addition, we propose(More)
An automatic human shape-motion analysis method based on a fusion architecture is proposed for human action recognition in videos. Robust shape-motion features are extracted from human points detection and tracking. The features are combined within the Transfer-able Belief Model (TBM) framework for action recognition. The TBM-based modelling and fusion(More)
Many applications are concerned by human action recognition notably in multimedia and more particularly for video retrieval and archival. Usual approaches focus on proba-bilistic methods and assume a still camera. In this paper , a method based on the Transferable Belief Model fusion process and considering a moving camera is proposed. In this framework,(More)
In this paper, we propose a framework for interactive image segmentation. The goal of interactive image segmentation is to classify the image pixels into foreground and background classes, when some foreground and background markers are given. The proposed method minimizes a min-max Bayesian criterion that has been successfully used on image segmentation(More)
This paper focuses on human behavior recognition where the main problem is to bridge the semantic gap between the analogue observations of the real world and the symbolic world of human interpretation. For that, a fusion architecture based on the Transfer-able Belief Model framework is proposed and applied to action recognition of an athlete in video(More)