Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.Comment: ACCV201

Four-dimensional dynamic flow measurement by holographic particle image velocimetry

The ultimate goal of holographic particle image velocimetry (HPIV) is to provide space- and time-resolved measurement of complex flows. Recent new understanding of holographic imaging of small particles, pertaining to intrinsic aberration and noise in particular, has enabled us to elucidate fundamental issues in HPIV and implement a new HPIV system. This system is based on our previously reported off-axis HPIV setup, but the design is optimized by incorporating our new insights of holographic particle imaging characteristics. Furthermore, the new system benefits from advanced data processing algorithms and distributed parallel computing technology. Because of its robustness and efficiency, for the first time to our knowledge, the goal of both temporally and spatially resolved flow measurements becomes tangible. We demonstrate its temporal measurement capability by a series of phase-locked dynamic measurements of instantaneous three-dimensional, three-component velocity fields in a highly three-dimensional vortical flow--the flow past a tab

On bubble clustering and energy spectra in pseudo-turbulence

3D-Particle Tracking (3D-PTV) and Phase Sensitive Constant Temperature Anemometry in pseudo-turbulence--i.e., flow solely driven by rising bubbles-- were performed to investigate bubble clustering and to obtain the mean bubble rise velocity, distributions of bubble velocities, and energy spectra at dilute gas concentrations ($\alpha \leq2.2$%). To characterize the clustering the pair correlation function $G(r,\theta)$ was calculated. The deformable bubbles with equivalent bubble diameter $d_b=4-5$ mm were found to cluster within a radial distance of a few bubble radii with a preferred vertical orientation. This vertical alignment was present at both small and large scales. For small distances also some horizontal clustering was found. The large number of data-points and the non intrusiveness of PTV allowed to obtain well-converged Probability Density Functions (PDFs) of the bubble velocity. The PDFs had a non-Gaussian form for all velocity components and intermittency effects could be observed. The energy spectrum of the liquid velocity fluctuations decayed with a power law of -3.2, different from the $\approx -5/3$ found for homogeneous isotropic turbulence, but close to the prediction -3 by \cite{lance} for pseudo-turbulence

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Towards automated visual flexible endoscope navigation

Background:\ud The design of flexible endoscopes has not changed significantly in the past 50 years. A trend is observed towards a wider application of flexible endoscopes with an increasing role in complex intraluminal therapeutic procedures. The nonintuitive and nonergonomical steering mechanism now forms a barrier in the extension of flexible endoscope applications. Automating the navigation of endoscopes could be a solution for this problem. This paper summarizes the current state of the art in image-based navigation algorithms. The objectives are to find the most promising navigation system(s) to date and to indicate fields for further research.\ud Methods:\ud A systematic literature search was performed using three general search terms in two medical–technological literature databases. Papers were included according to the inclusion criteria. A total of 135 papers were analyzed. Ultimately, 26 were included.\ud Results:\ud Navigation often is based on visual information, which means steering the endoscope using the images that the endoscope produces. Two main techniques are described: lumen centralization and visual odometry. Although the research results are promising, no successful, commercially available automated flexible endoscopy system exists to date.\ud Conclusions:\ud Automated systems that employ conventional flexible endoscopes show the most promising prospects in terms of cost and applicability. To produce such a system, the research focus should lie on finding low-cost mechatronics and technologically robust steering algorithms. Additional functionality and increased efficiency can be obtained through software development. The first priority is to find real-time, robust steering algorithms. These algorithms need to handle bubbles, motion blur, and other image artifacts without disrupting the steering process