Abstract: A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt?1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt?1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detectio

Abstract: A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt?1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt?1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detectio

Abstract: The systems described herein include tools for computer-assisted evaluation of objective characteristics of pathologies. A diagnostic system arranged according to the teachings herein provides computer support for those tasks well suited to objective analysis, along with human decision making where substantial discretion is involved. Collaborative diagnosis may be provided through shared access to data and shared control over a diagnostic tool, such as a telemicroscope, and messaging service for clinicians who may be at remote locations. These aspects of the system, when working in cooperation with one another, may achieve improved diagnostic accuracy or early detection for pathologies such as lymphoma.

Abstract: The systems described herein include tools for computer-assisted evaluation of objective characteristics of pathologies. A diagnostic system arranged according to the teachings herein provides computer support for those tasks well suited to objective analysis, along with human decision making where substantial discretion is involved. Collaborative diagnosis may be provided through shared access to data and shared control over a diagnostic tool, such as a telemicroscope, and messaging service for clinicians who may be at remote locations. These aspects of the system, when working in cooperation with one another, may achieve improved diagnostic accuracy or early detection for pathologies such as lymphoma.

Abstract: In an axial-flow turbine consisting essentially of an outer casing, an inner casing with a preferably integrated vane carrier (5) and a rotor (3) fitted with rotor blades (6), the first stage is designed as a radial/axial stage. It is supplied with working medium from a toroidal or spiral inlet flow housing (4). The radial vanes (10) are provided at their two ends with root plates (14) by means of which they are bladed in annular recesses (15) in the blade carrier (5). The free end faces (19) of the root plates are of curved design.