Kazuo Ohmi

New algorithms of 3D particle tracking velocimetry (3D PTV) based on a tomographic reconstruction approach have been developed and tested by using synthetic images of unsteady 3D flows. The new algorithms are considered not only in the tomographic reconstruction process of the fluid volume with particles but also in the subsequent process of individual particle detection and validation. In particular, the tomographic reconstruction accuracy is boosted up by using a new recursive validation scheme through which many of ghost particles can be removed effectively. The particle detection process includes the particle mask correlation operator and the dynamic threshold scheme to extract individual particle centroids from the reconstructed intensity clusters of the fluid volume. The overall reconstruction accuracy is checked by the synthetic image data sets with different particle density and different volume thickness.

A new technique for the particle depth and size determination is implemented and tested by using synthetic as well as experimental hologram images. The particle depth and size are detected by geometric measures of the particle signal peak in the reconstructed images. Performance tests are carried out with different holograms patterns consisting of different sizes particles and overlapping interference fringes.

An ant colony optimization (ACO) based stereoscopic particle matching algorithm has been developed for three-dimensional (3-D) particle tracking velocimetry (PTV). In stereoscopic particle pairing process, each individual particle in the left camera frame should be uniquely paired with the most probable correct partner in the right camera frame or vice-versa for evaluating the exact 3-D coordinate of the particles. In the present work, a new algorithm based on an ant colony optimization has been proposed for this stereoscopic particle matching.

This paper presents two different novel techniques for the detection of the depth of small tracer particles distributed in 3-D space. These techniques are based on in-line holography and the depth of the particles in both cases is measured using the numerically reconstructed images obtained from the convolution based Fresnel reconstruction formula.

A new concept algorithm based on the ant colony optimization is developed for the use in 2-D and 3-D particle tracking velocimetry (PTV). In the particle matching process of PTV, the ant colony optimization is usually aimed at minimization of the sum of the distances between the first-frame and second-frame particles. But this type of minimization often goes unsuccessfully in the regions where the particles are located very close to each other. In order to avoid this flaw, a new type of minimization is attempted using a physical property corresponding to the flow consistency or the quasi-rigidity of particle distribution patterns. Specifically, the ant colony optimization is now aimed at minimization of the sum of the relaxation of neighbor particles. In the present study, the new algorithm is applied to sets of 2-D and 3-D synthetic particle images as well as the experimental images with successful results.

Particle Racking Velocimetry (PTV) is a tool to measure the velocity fields of fluids by observing the motion of the tracer particles seeded in them. The displacement of individual particles gives the velocity information if divided by a known time interval. The accuracy as well as efficiency of the PTV systems depend upon the reliability of the algorithms to track the motion of those particles. There exist different algorithms for this purpose but there is a lack of a standard system to judge or compare the accuracy of the results obtained. The velocity information is always prone to outliers. The outliers degrade the quantitative information of the velocity field and gives misleading information of velocity based quantities like vorticity, streamlines, divergence etc. In this paper, a technique based on rule-based fuzzy logic has been proposed for the detection of such outliers. A novel evaluation scheme is proposed which. is applicable to test the reliability of the given algorithms.

A self-organizing map (SOM) based algorithm has been developed for 3-D particle tracking velocimetry (3-D PTV) in stereoscopic particle pairing process. Every particle image in the left-camera frame should be paired with the most probably correct partner in the right-camera frame or vice versa for evaluating the exact coordinate.

A new concept genetic algorithm has been implemented and tested for the use in the 2-D and 3-D Particle Tracking Velocimetry. The algorithm is applicable to particle images with larger (greater than 2000) number of particles without losing the excellent accuracy in the particle matching results.

Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time.

The self-organizing maps (SOM) neural network is applied to the particle matching algorithm of the 3D particle tracking velocimetry (PTV). In the particle tracking velocimetry, the matching result of particles between two time-differential image frames is directly related to the velocity of particles, i.e., the velocity of the fluid flow in which the particles are suspended. The new particle matching method is basically based on the SOM model by Labonte [G. Labonte, A new neural network for particle tracking velocimetry, Experiments in Fluids 26-4 (1999) 340-346] but has been improved in many aspects for more reliable matching at larger numbers of distributed particles, larger dynamic range of velocity and more robustness against loss-of-pair particles between two image frames. In addition the new method is now applied to 3D particle flows for the use in 3D particle tracking velocimetry. In the present study, the new method is tested with 2D and 3D synthetic particle images as well as with 2D experimental images with a large number of particles. (C) 2008 Elsevier Inc. All rights reserved.

Particle tracking velocimetry (PTV) is a reliable measurement technique for the quantitative study of fluid flows by observing the motion of the particles seeded in them and is widely used in several industrial applications. The nature of the flow can be precisely observed only if all the three components of the velocity are computed. In 3-D PTV system, particles viewed by two (or more than two) stereoscopic cameras with a parallax have to be correctly paired at every synchronized time step. This is important because the 3-D coordinates of individual particles cannot be computed without the knowledge of the correct stereo correspondence of the particles. In the present work, a neural network-based algorithm has been proposed for the stereoscopic particle pairing process. The correspondence between the particle pairs is modeled as a constrained optimization problem. The constraints are provided on the basis of the epipolar geometry of the particle images and on the basis of the uniqueness of the matched pairs. The results are tested with various standard images. (C) 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Color and luminance expression of artistic master paintings is investigated by a digital vector scope and a wave form analyzer working on the digital reproduction of the original paintings. Examples are taken from the representatives of the 19th century Rococo-romantic school, the Romantic school, the early and late Impressionist schools including the Pointism and Symbolism paintings. The results are then compared in terms of the hue and chroma distribution pattern as well as of the brightness characteristics between two or more paintings.

A new method of particle image detection has been implemented and tested for the use in the particle tracking velocimetry. The method benefits from a new algorithm of extracting individual particle images from a massive concentration of particles recorded in the PIV recordings. With this algorithm, the binary threshold level is determined particle by particle in such a way that the mean gray level of every particle is kept at a constant degree of contrast against the background. So the particles of any size, shape or gray levels are extracted. In addition, the algorithm is applicable to a variety of particle images, including those suffering from background noise. In the latter half of the paper, the new algorithm is further improved in order to speed up the computation and facilitate the estimation of the preset parameters.

The dynamic threshold binarization method has been implemented and tested for the use in the particle tracking velocimetry. The method aims to extract the individual particle images from a massive concentration of particles recorded in the PIV recordings. In order to reduce the empirical manipulation of this binarization method, the optimization of the two input parameters is attempted with the aid of modem image analysis techniques. The results, especially those with the Otsu method for the first input parameter, are quite successful in the sense that this method of binarization is more easily applicable to ordinary particle images with a certain degree of background noise.

Thanks to high-power and low-cost PCs, the image processing, which required a grand-scale system in old days, is now in reach of everyone's hand. Especially the windows system on this PC platform has experienced such development, that even a personally made image processing program should surpass specialized commercial software with respect to computing and displaying performance. This paper presents some typical fluid-oriented visualization and image processing software developed by the authors, which includes animation of experimental flow visualization images, animation of numerical flow visualizations, some basic PIV/PTV measurement results and so on.

Thanks to high-power and low-cost PCs, the image processing, which required a grand-scale system in old days, is now in reach of everyone's hand. Especially the windows system on this PC platform has experienced such development, that even a personally made image processing program should surpass specialized commercial software with respect to computing and displaying performance. This paper presents some typical fluid-oriented visualization and image processing software developed by the authors, which includes animation of experimental flow visualization images, animation of numerical flow visualizations, some basic PIV/PTV measurement results and so on.

New PTV measurement using a Fourier-Transform phase correlation method is attempted on the experimental flows showing a 2-D circular cylinder wake. To make a good use of the qualities of this phase correlation method, the image templates for pattern matching are not described by the original particle dots, but by single polygons connecting all the particles in each template, followed by 2-D normal FFT and phase-component inverse FFT processes. The results obtained are not so revo-lutionary when compared to ordinary direct correlation methods, but there exists apparent improvement in the rotating flow regions.