Marek Schikora

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Shape optimization is a problem which arises in numerous computer vision problems such as image segmentation and multiview reconstruction. In this paper, we focus on a certain class of binary labeling problems which can be globally optimized both in a spatially discrete setting and in a spatially continuous setting. The main contribution of this paper is to(More)
Multi-target tracking is a common problem with many applications. In most of these the expected number of targets is not known a priori, so that it has to be estimated from the measured data. Poisson point processes (PPPs) are a very useful theoretical model for diverse applications. One of those is multi-target tracking of an unknown number of targets,(More)
Poisson point processes (PPP's) are very useful theoretical models for diverse applications. One of those is multi-target tracking of an unknown number of targets, leading to the intensity filter (iFilter) as a generalization of the probability hypothesis density (PHD) filter. This article develops a sequential Monte Carlo (SMC) implementation of the(More)
The ability of plants to monitor their surroundings, for instance the perception of bacteria, is of crucial importance. The perception of microorganism-derived molecules and their effector proteins is the best understood of these monitoring processes. In addition, plants perceive bacterial quorum sensing (QS) molecules used for cell-to-cell communication(More)
This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable to deal with three sources of uncertainty: stochastic, set-theoretic and data association(More)
In this work we present a novel method for tracking an unknown number of objects with a single camera system in real-time. The proposed algorithm is based on high-accuracy optical flow and finite set statistics. In this framework the target state is treated as a random vector and the number of possible objects as a random number, which has to be estimated(More)
This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association(More)
Detection of cars has a high variety of civil and military applications, e.g., transportation control, traffic monitoring, and surveillance. It forms an important aspect in the deployment of autonomous unmanned aerial systems in rescue or surveillance missions. In this paper, we present a two-stage algorithm for detecting automobiles in aerial digital(More)
In this paper, we present a graph based approach for performing the system calibration of a sensor suite containing a fixed mounted camera and an inertial navigation system. The aim of the presented work is to obtain accurate direct georeferencing of camera images collected with small unmanned aerial systems. Prerequisite for using the pose measurements(More)
This paper investigates the three-dimensional localization problem for multiple emitters using a realistic airborne array sensor. Three sensor models are considered: the ideal array model, an array sensor with a constant bias, and the realistic array model with bias errors that depend on the signal direction of arrival itself. The realistic array model(More)