Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach

This paper proposes a probabilistic approach for the detection and the tracking of particles in fluorescent time-lapse imaging. In the presence of a very noised and poor-quality data, particles and trajectories can be characterized by an a contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that neither require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well-established baseline show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application

Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground

On noise processes and limits of performance in biosensors

In this paper, we present a comprehensive stochastic model describing the measurement uncertainty, output signal, and limits of detection of affinity-based biosensors. The biochemical events within the biosensor platform are modeled by a Markov stochastic process, describing both the probabilistic mass transfer and the interactions of analytes with the capturing probes. To generalize this model and incorporate the detection process, we add noisy signal transduction and amplification stages to the Markov model. Using this approach, we are able to evaluate not only the output signal and the statistics of its fluctuation but also the noise contributions of each stage within the biosensor platform. Furthermore, we apply our formulations to define the signal-to-noise ratio, noise figure, and detection dynamic range of affinity-based biosensors. Motivated by the platforms encountered in practice, we construct the noise model of a number of widely used systems. The results of this study show that our formulations predict the behavioral characteristics of affinity-based biosensors which indicate the validity of the model