Automated visual-tracking of cell populations in vitro using time-lapse phase contrast microscopy enables quantitative, systematic and high-throughput measurements of cell behaviors. These measurements include the spatiotemporal quantification of cell migration, mitosis, apoptosis, and the reconstruction of cell lineages. The combination of low signal-to-noise ratio of phase contrast microscopy images, high and varying densities of the cell cultures, topological complexities of cell shapes, and wide range of cell behaviors poses many challenges to existing tracking techniques. This paper presents a fully-automated multi-target tracking system that can efficiently cope with these challenges while simultaneously tracking and analyzing thousands of cells observed using time-lapse phase contrast microscopy. The system combines bottom-up and top-down image analysis by integrating multiple collaborative modules, which exploit a fast geometric active contour tracker in conjunction with adaptive interacting multiple models (IMM) motion filtering and spatiotemporal trajectory optimization. The system, which was tested using a variety of cell populations, achieved tracking accuracy in the range of 86.9%-92.5%.

... without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods---the use of multiple models (and filters) simultaneously---which is the prevailing approach to maneuvering target tracking in the recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes on the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.

This paper concerns the sequential estimation of a hidden state vector from noisy observations delivered by several sensors. Different from the standard framework, we assume here that the sensors may switch autonomously between different sensor states, that is, between different observation models. This includes sensor failure or sensor functioning conditions change. In our model, sensor states are represented by discrete latent variables, which prior probabilities are Markovian. We propose a family of efficient particle filters, for both synchronous and asynchronous sensor observations, as well as for important special cases. Moreover, we discuss connections with previous works. Finally, we study thoroughly a wheel land vehicle positioning problem where the GPS information may be unreliable because of multipath/masking effects. EDICS: SEN- FUS

Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors' results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures' estimation. Monte Carlo simulation results of TMP-adaptive interacting multiple model algorithms for a system with failures and maneuvering target tracking are presented.

The virtual white cane is a range sensing device based on active triangulation, that can measure distances at a rate of 15 measurements/second. A blind person can use this device for sensing the environment, pointing it as if it was a flashlight. Beside measuring distances, this device can detect surface discontinuities, such as the foot of a wall, a step, or a drop-off. This is obtained by analyzing the range data collected as the user swings the device around, tracking planar patches and finding discontinuities. In this paper we briefly describe the range sensing device, and present an online surface tracking algorithm, based on a Jump-Markov model. We show experimental results proving the robustness of the tracking system in real-world conditions. 1.

Abstract—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index and a maneuvering target tracking application. Index Terms—Autoregressive processes, Bayesian methods, hidden Markov models, state-space methods, time series analysis,

Abstract — This paper extends a recently developed statistical framework for UAV search with uncertain probability maps to the case of dynamic targets. The probabilities used to encode the information about the environment are typically assumed to be exactly known in the search theory literature, but they are often the result of prior information that is both erroneous and delayed, and will likely be poorly known to mission designers. Our previous work developed a new framework that accounted for the uncertainty in the probability maps for stationary targets, and this paper extends the approach to more realistic dynamic environments. The dynamic case considers probabilistic target motion, creating Uncertain Probability Maps (UPMs) that take into account both poor knowledge of the probabilities and the propagation of their uncertainty through the environment. A key result of this paper is a new algorithm for implementing UPM’s in real-time, and it is shown in various simulations that this algorithm leads to more cautious information updates that are less susceptible to false alarms. The paper also provides insights on the impact of the design parameters on the responsiveness of the new algorithm. Several numerical examples are presented to demonstrate the effectiveness of the new framework. I.

Actual performance of sequential decision-making problems can be extremely sensitive to errors in the models, and this research addressed the role of robustness in coping with this uncertainty. The first part of this thesis presents a computationally efficient sampling methodology, Dirichlet Sigma Points, for solving robust Markov Decision Processes with transition probability uncertainty. A Dirichlet prior is used to model the uncertainty in the transition probabilities. This approach uses the first two moments of the Dirichlet to generates samples of the uncertain probabilities and