Lymphocite segmentation using mixture of Gaussians and the transferable belief model.

International audienceIn the context of several pathologies, the presence of lym- phocytes has been correlated with disease outcome. The ability to au- tomatically 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 princi- pal image component. Then, a post-processing stage eliminates regions, whose shape is not similar to the nuclei searched. Finally, the Transfer- able Belief Model is used to detect the lymphocyte nuclei, and a shape based algorithm possibly splits them under an equal area and an eccen- tricity constraint principle

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

International audienceA tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided

Segmentation and sampling of moving object trajectories based on representativeness.

International audienceMoving 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 sub-trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub-trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques

Human Shape-Motion Analysis In Athletics Videos for Coarse To Fine Action/Activity Recognition Using Transferable BeliefModel

We present an automatic human shape-motion analysis method based on a fusion architecture for human action and activity recognition in athletic videos. Robust shape and motion features are extracted from human detection and tracking. The features are combined within the Transferable Belief Model (TBM framework for two levels of recognition. The TBM-based modelling of the fusion process allows to take into account imprecision, uncertainty and conflict inherent to the features. First, in a coarse step, actions are roughly recognized. Then, in a fine step, an action sequence recognition method is used to discriminate activities. Belief on actions are made smooth by a Temporal Credal Filter and action sequences, i.e. activities, are recognized using a state machine, called belief scheduler, based on TBM. The belief scheduler is also 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 actions (running, jumping, falling and standing) and four types of activities (high jump, pole vault, triple jump and long jump). Results on actions, activities and feedback demonstrate the relevance of the proposed features and as well the efficiency of the proposed recognition approach based on TBM

Multidisciplinary oil spill modeling to protect coastal communities and the environment of the Eastern Mediterranean Sea

We present new mathematical and geological models to assist civil protection authorities in the mitigation of potential oil spill accidents in the Eastern Mediterranean Sea. Oil spill simulations for 19 existing offshore wells were carried out based on novel and high resolution bathymetric, meteorological, oceanographic, and geomorphological data. The simulations show a trend for east and northeast movement of oil spills into the Levantine Basin, affecting the coastal areas of Israel, Lebanon and Syria. Oil slicks will reach the coast in 1 to 20 days, driven by the action of the winds, currents and waves. By applying a qualitative analysis, seabed morphology is for the first time related to the direction of the oil slick expansion, as it is able to alter the movement of sea currents. Specifically, the direction of the major axis of the oil spills, in most of the cases examined, is oriented according to the prevailing azimuth of bathymetric features. This work suggests that oil spills in the Eastern Mediterranean Sea should be mitigated in the very few hours after their onset, and before wind and currents disperse them. We explain that protocols should be prioritized between neighboring countries to mitigate any oil spills

Modelling of oil spills in confined maritime basins: The case for early response in the Eastern Mediterranean Sea

Oil spill models are combined with bathymetric, meteorological, oceanographic, and geomorphological data to model a series of oil spill accidents in the Eastern Mediterranean Sea. A total of 104 oil spill simulations, computed for 11 different locations in the Levantine Basin, show that oil slicks will reach the coast of Cyprus in four (4) to seven (7) days in summer conditions. Oil slick trajectories are controlled by prevailing winds and current eddies. Based on these results, we support the use of chemical dispersants in the very few hours after large accidental oil spills. As a corollary, we show shoreline susceptibility to vary depending on: a) differences in coastline morphology and exposure to wave action, b) the existence of uplifted wave-cut platforms, coastal lagoons and pools, and c) the presence of tourist and protected environmental areas. Mitigation work should take into account the relatively high susceptibility of parts of the Eastern Mediterranean

Ανάλυση κίνησης και μοντελοποίησης για αναγνώριση δραστηριότητας και 3-Δ συνθετική κίνηση βασισμένη σε γεωμετρικούς αλγορίθμους και μεθόδους επεξεργασίας βίντεο

The analysis of audiovisual data aims at extracting high level information, equivalent with the one(s) that can be extracted by a human. It is considered as a fundamental, unsolved (in its general form) problem. Even though the inverse problem, the audiovisual (sound and animation) synthesis, is judged easier than the previous, it remains an unsolved problem. The systematic research on these problems yields solutions that constitute the basis for a great number of continuously developing applications. In this thesis, we examine the two aforementioned fundamental problems. We propose algorithms and models of analysis and synthesis of articulated motion and undulatory (snake) locomotion, using data from video sequences. The goal of this research is the multilevel information extraction from video, like object tracking and activity recognition, and the 3–D animation synthesis in virtual environments based on the results of analysis. An important part of this thesis is dedicated to automatic human motion analysis from video and action/activity recognition. Moreover, we examine the problem of animal animation synthesis using parametric models and algorithms that are based on motion tracking data over appropriate video sequences. The problem of undulatory locomotion analysis led in the definition of a general geometrical problem, the curve EquiPartition (EP). In this thesis, we define, analyze and solve the EP problem. In the case of human motion analysis, we propose a general framework that focuses on automatic individual/multiple people motion-shape analysis and on suitable features extraction, which can be used on action/activity recognition problems under real, dynamical and unconstrained environments. In order to evaluate the robustness of the proposed scheme, we have applied it on various athletic videos from a single uncalibrated, possibly moving camera. The automatic analysis of these videos is a challenging problem due to the complex and fast motions of the athletes and to the unconstrained changes in the environment of athletic meetings. More specifically, an easily expanded hierarchical architecture is proposed, so that a video sequence is classified to video of individual and team sport. Afterwards, the corresponding methods of motion-shape analysis are used recognizing the activity (current sport like high jump, long jump, hurdling, etc.) and the phase (action) at each time (like running, jumping, etc.). Concerning the animals’ animation synthesis, we have examined the problem of animal modeling, 3–D model construction and 3–D animation synthesis in complex 3–D virtual environments. The motion analysis and the 3–D animal model construction are performed using videos captured by a static camera from specific viewpoints. We have proposed distinct methods for articulated and undulatory motion analysis and synthesis. We have applied our articulated motion synthesis methodology for birds (of which we have synthesized an eagle), for reptiles (of which we have synthesized a lizard) and for mammals (of which we have synthesized a goat). The whole methodology can be easily expanded in similar creatures. Concerning the undulatory locomotion, we have proposed an efficient modeling with minimal number of coefficients for noise reduction. Finally, a general planning algorithm on a state graph is proposed for aperiodic and unseen locomotion synthesis. The general geometrical problem of partitioning a continuous curve into N parts with equal chords, under any metric distance, the curve EquiPartition problem (EP), is analyzed and solved. Moreover, we prove that the problem admits at least one solution under the Euclidean distance metric for planar curves, based on an introduced equivalent problem definition, the Level Set Approach. Finally, EP based applications, like polygonal approximation and key frames selection, are presented, and the special properties of their solutions are discussed