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

Human motion analysis and simulation tools: a survey

Computational systems to identify objects represented in image sequences and tracking their motion in a fully automatic manner, enabling a detailed analysis of the involved motion and its simulation are extremely relevant in several fields of our society. In particular, the analysis and simulation of the human motion has a wide spectrum of relevant applications with a manifest social and economic impact. In fact, usage of human motion data is fundamental in a broad number of domains (e.g.: sports, rehabilitation, robotics, surveillance, gesture-based user interfaces, etc.). Consequently, many relevant engineering software applications have been developed with the purpose of analyzing and/or simulating the human motion. This chapter presents a detailed, broad and up to date survey on motion simulation and/or analysis software packages that have been developed either by the scientific community or commercial entities. Moreover, a main contribution of this chapter is an effective framework to classify and compare motion simulation and analysis tools

Shape-based individual/group detection for sport videos categorization

International audienceWe present a shape based method for automatic people detection and counting without any assumption or nowledge of camera motion. The proposed method is applied to athletic videos in order to classify them to videos of individual and team sports. Moreover, in the case of team (multi-agent) sport, we propose a shape deformations based method for running/hurdling discrimination (activity recognition). Robust, adaptive and independent from the camera motion, the proposed features are combined within the Transferable Belief Model (TBM) framework providing a two level (frames and shot) video categorization. The TBM allows to take into account imprecision, uncertainty and conflict inherent to the features into the fusion process.We have tested the proposed scheme into a big variety of athletic videos like pole vault, high jump, triple jump, hurdling, running, etc. The experimental results of 97% individual/team sport categorization accuracy, using a dataset of 252 real videos of athletic meetings acquired by moving cameras under varying view angles, indicate the stability and the good performance of the proposed scheme