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In the context of several pathologies, the presence of lymphocytes has been correlated with disease outcome. The ability to automatically detect lymphocyte nuclei on histopathology imagery could potentially result in the development of an image based prognostic tool. In this paper we present a method based on the estimation of a mixture of Gaussians for determining the probability distribution of the principal image component. Then, a post-processing stage eliminates regions, whose shape is not similar to the nuclei searched. Finally, a Transferable Belief Model is used to detect the lymphocyte nuclei, and a shape based algorithm possibly splits them under an equal area and an eccentricity constraint principle. [ABSTRACT FROM AUTHOR]

A novel methodology for efficiently sampling Trajectory Databases (TD) for mobility data mining purposes is presented. In particular, a three-step unsupervised trajectory sampling methodology is proposed, that initially adopts a symbolic vector representation of a trajectory which, using a similarity-based voting technique, is transformed to a continuous function that describes the representativeness of the trajectory in the TD. This vector representation is then relaxed by a merging algorithm, which identifies the maximal representative portions of each trajectory, at the same time preserving the space-time mobility pattern of the trajectory. Finally, a novel sampling algorithm operating on the previous representation is proposed, allowing us to select a subset of a TD in an unsupervised way encapsulating the behavior (in terms of mobility patterns) of the original TD. An experimental evaluation over synthetic and real TD demonstrates the efficiency and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

We propose a method for trajectory classification based on trajectory voting in Moving Object Databases (MOD). Trajectory voting is performed based on local trajectory similarity. This is a relatively new topic in the spatial and spatiotemporal database literature with a variety of applications like trajectory summarization, classification, searching and retrieval. In this work, we have used moving object databases in space, acquiring spatiotemporal 3-D trajectories, consisting of the 2-D geographic location and the 1-D time information. Each trajectory is modelled by sequential 3-D line segments. The global voting method is applied for each segment of the trajectory, forming a local trajectory descriptor. By the analysis of this descriptor the representative paths of the trajectory can be detected, that can be used to visualize a MOD. Our experimental results verify that the proposed method efficiently classifies trajectories and their sub-trajectories based on a robust voting method. [ABSTRACT FROM AUTHOR]

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 Transferable Belief Model (TBM) framework for action recognition. The TBM-based modelling and fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. Action recognition is performed by a multilevel analysis. The sequencing is exploited for feedback information extraction in order to improve tracking results. The system is tested on real videos of athletics meetings to recognize four types of jumps: high jump, pole vault, triple jump and long jump. [ABSTRACT FROM AUTHOR]