Markus Brandner

A particle filter approach for tomographic imaging based on different state-space representations

In electrical capacitance tomography (ECT) the main focus is on the reconstruction of distinct objects with sharp transitions between different phases. Being inherently ill-posed, the reconstruction algorithm requires some sort of regularization to stabilize the solution of the inverse problem. However, introducing regularization may counteract the reconstruction of well-defined contours for grid-based methods. Level set propagation approaches which also rely on regularization are able to model sharp phase boundaries but suffer from high computational demands. In this contribution, two different state-space representations of closed contours based on B-splines and on Fourier descriptors are investigated. Both approaches allow us to describe the problem with only a small set of state-space variables. Regularization is incorporated implicitly which can be directly interpreted in the object domain as it relates to smooth contours. To solve the inverse problem, statistical inversion is performed by means of particle filtering providing the opportunity to conveniently incorporate prior information and to take measurement uncertainties into account. The proposed particle filter approach is compared to an extended Kalman filter realization in terms of complexity, computation time and estimation accuracy.

Experimental evaluation of RFID gate concepts

The extended reader ranges in the UHF band enable many new applications of RFID systems in the logistic sector. However, this also introduces false positive reads which can cause severe performance issues in the backend system. The performance of RFID gates as basic building blocks of RFID systems directly determines the overall behavior of the RFID application. Approaches to improve the robustness of gates with respect to false positive reads include the heuristic incorporation of proximity sensors and the use of multiple antennae. In this paper we present a comparison of three gate concepts which differ in complexity of the underlying algorithms and hardware requirements. We experimentally evaluate the performance of these gates in a standard conveyor belt application under real-world conditions.

Improving classification performance of RFID gates using Hidden Markov models

Many applications ranging from warehouse logistics to DVD rental require the robust localization and tracking of goods on the item level. RFID represents a popular technology to perform these tasks but suffers from false read events — especially in the widely used UHF range. This paper presents a probabilistic model for an RFID gate. The novelty of our approach is the incorporation of both the uncertainties introduced by the random nature of reader inventories as well as process knowledge into a single well-defined probabilistic framework based on HMMs. The robustness of the resulting tag detection algorithm against stationary tags in the vicinity of the gate is shown experimentally using a demonstrator RFID gate setup.

Uncertainty Estimation in a Vision-Based Tracking System

Vision-based tracking is concerned with the recovery of position and orientation data of moving objects based on visual input provided by one or more cameras. This paper describes a framework to handle geometric parameter uncertainties within a monocular outside-in vision-based tracking application. We present a sensor model - the stochastic camera - that is capable to take parameter calibration uncertainties into consideration even under real-time requirements. The feasibility of the proposed method is shown in closed-loop tracking experiments

Probabilistic modeling of RFID business processes

Radio Frequency Identification has gained increasing popularity in the logistic sector as a tool to locate and track individual items throughout complex processes. A characteristic of RFID systems is that both read events and business processes can be subjected to uncertainties. In this work we present a novel modeling approach for RFID processes and supply chains. The proposed model considers typical trajectories of tags through a business process and can be used to improve the detection performance of individual RFID gates. Consequently, this improves the robustness of the overall RFID system.

Real-Time Tracking of Complex Objects Using Dynamic Interpretation Tree

Vision-based tracking for augmented reality (AR) applications requires highly accurate position and pose measurements at video frame rate. Typically several interaction devices have to be tracked simultaneously. While the geometry of all devices and the spatial layout of visual landmarks on the devices are well known, problems of occlusion as well as of prohibitively large search spaces remain to be solved. The main contribution of the paper is in high-level algorithms for real-time tracking. We describe a model-based tracking system which implements a dynamic extension of the structure of an interpretation tree for scene analysis. This structure is well suited to track multiple rigid objects in a dynamic environment. Independent of the class of low-level features being tracked, the algorithm is capable to handle occlusions due to a model-dependent recovery strategy. The proposed high-level algorithm has been applied to stereo-based outside-in optical tracking for AR. The results show the ability of the dynamic interpretation tree to cope with partial or full object occlusion and to deliver the required object pose parameters at a rate of 30 Hz.

Vision-Based Control of an Inverted Pendulum using Cascaded Particle Filters

The inverted pendulum represents an example of a nonlinear and unstable dynamic system whose properties and related control strategies have been studied extensively in the control literature. A basic setup consists of a translation device - the cart - and a rotating boom - the pendulum - which is balanced in its upright position. The control algorithm uses the position of the cart and the rotation angle of the pendulum as input measurements provided by different sensors. While contacting sensor principles such as angle encoders have been used in different demonstrators, we present a purely vision-based tracking system. In combination with state of the art control algorithms our system is able to swing-up and balance the pendulum. The presented setup is non-contacting and does not require specific visual markers on any part of the pendulum. We use off-the-shelf components to realise the monocular tracking system. Experimental results show that our system is able to robustly control the inverted pendulum in real-time on a standard desktop PC.

A measurement device for the comparative evaluation of proximal teeth contact strengths

Interdental contact forces are important parameters in dentistry. This paper presents an indirect method for the evaluation of contact forces as it is difficult to directly measure the contact strength on a patient. Our method is based on the measurement of forces required to insert and remove a test body into the interdental space. The proposed sensor principle allows us to perform a qualitative comparison of contact strengths of neighbouring teeth. We use a dental floss mounted in a replaceable fork as a disposable test body. Through the application of specific signal processing algorithms we automatically extract the time instances of the test body insertion and retraction and can therefore provide a hand-guided sensor without the need for a fixed geometric reference on the patient.

Optical 2D Displacement and Strain Sensor for Creep Testing of Material Samples in Transparent Fluids

The designer of mechanical products needs to know the behaviour of the applied materials under different environmental conditions. A standard experiment used to determine these behavioural parameters is the creep test. Certain groups of materials such as polymers exhibit large sensitivities of these parameters with respect to environmental conditions such as temperature and exposure to chemicals. In these cases creep tests need to be performed under conditions that match as closely as possible the service conditions, i.e. conditions under which the material will be used. The physical measurands observed during theses tests are displacement and consequently strain. In general, it is difficult to achieve the long-term stability and accuracy required by creep tests as these experiments usually last for more than thousand hours. The present paper reports on the design and implementation of an optical sensor for 2D displacement and strain measurements in transparent fluids. The proposed sensor meets the accuracy requirements imposed by the creep test procedure. Further, the sensor is insensitive to homogeneous changes in the optical path and thus is well suited for long-term displacement measurements in transparent fluids.

Dynamic image reconstruction in electrical capacitance tomography using particle filters

Electrical capacitance tomography (ECT) is a non-invasive imaging technique which is used to acquire information about the spatial material distribution of inaccessible objects in order to monitor industrial processes such as two-phase flow fields in the oil industry. In this contribution, the application of a particle filter (PF) to solve the inverse ECT problem is proposed. PFs offer the possibility to tackle time varying objects during the reconstruction task given uncertain measurements. By implementing a filter bank, the merging and splitting of bubbles which is frequently encountered in dynamic flow processes is taken into account. The novelty of the proposed sensor lies in the multimodal state-space representation that includes object motion and dynamic contours and is able to cover a variety of physically meaningful transitions. The signal processing algorithm is validated using measured data