Two-Dimensional Flow Field Measurement Method for Sediment-Laden Flow Based on Optical Flow Algorithm

Particle image velocimetry (PIV) has matured as a flow measurement technique. It enables the description of the instantaneous velocity field of the flow by analyzing the particle motion obtained from digitally recorded images. Correlation based PIV evaluation technique is widely used because of its good accuracy and robustness. Although very successful, correlation PIV technique has some weakness which can be avoided by optical flow based PIV algorithms. At present, most of the optical flow methods applied to PIV are based on brightness constancy assumption. However, some factors of flow imaging technology and the nature property of the fluids make the brightness constancy assumption less appropriate in real PIV cases. In this paper, an implementation of a 2D optical flow algorithm (GCOF) based on gradient constancy assumption is introduced. The proposed GCOF assumes the edges of the illuminated PIV particles are constant during motion. It comprises two terms: a combined local-global gradient data term and a first-order divergence and vorticity smooth term. The approach can provide accurate dense motion fields. The approach are tested on synthetic images and on two experimental flows. The comparison of GCOF with other optical flow algorithms indicates the proposed method is more accurate especially in conditions of illumination variation. The comparison of GCOF with correlation PIV technique shows that the proposed GCOF has advantages on preserving small divergence and vorticity structures of the motion field and getting less outliers. As a consequence, the GCOF acquire a more accurate and better topological description of the turbulent flow.

Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion

In this paper we present an approach for the efficient computation of optical flow fields in real-time and provide implementation details. Proposing a modification of the popular Lucas-Kanade energy functional based on integral projections allows us to speed up the method notably. We show the potential of this method which can compute dense flow fields of 640×480 pixels at a speed of 4 fps in a GPU implementation based on the OpenCL framework. Working on sparse optical flow fields of up to 17,000 points, we reach execution times of 70 fps. Optical flow methods are used in many different areas, the proposed method speeds up current surveillance algorithms used for scene description and crowd analysis or Augmented Reality and robot navigation applications.

Obstacle detection by evaluation of optical flow fields from image sequences

In this paper, we consider a motion sense problem motivated by the International Aerial Robotics Competition Mission-7, where an aerial robot is required to provide detection and estimation about mobile vehicles. Dense optical flow computing is employed first to provide a velocity field from image sequences. Then, region growing based on the optical flow field is used to extract moving objects on the background, and motion estimation is eventually achieved while both camera and objects are moving. In addition, classical optical flow techniques do not work in the competition since there may be illumination changes, such as flashlights and reflections in the arena. To deal with this problem, the procedures of the brightness constancy relaxation and intensity normalization are combined in the optical flow algorithm. Experimental results have demonstrated the robustness against varying illumination. The proposed approach can provide motion estimation results of acceptable accuracy for several benchmark data sets and image sequences generated with micro aerial vehicles.

Effects of free surface on development of turbulent boundary layer and wake fields were investigated. By measuring flow field around a surface piercing cylinder in various advance speed conditions in a towing tank, free surface effects were identified. A towed underwater Stereoscopic Particle Image Velocimetry (SPIV) system was used to measure the flow field under free surface. The cross section of the test model was water plane shape of the Wigley hull, of which longitudinal length and width were 1.0 m and 100 mm, respectively. With sharp bow shape and slender cross section, flow separation was not expected in two-dimensional flow. Flow fields near the free-surface and in deep location that two-dimensional flow field was expected were measured and compared to identify free-surface effects. Some planes perpendicular to longitudinal direction near the model surface and behind the model were selected to track development of turbulent boundary layer. Froude numbers of the test conditions were from 0.126 to 0.40 and corresponding Reynolds numbers were from 395,000 to 1,250,000. In the lowest Froude number condition, free-surface wave was hardly observed and only free surface effects without surface wave could be identified while violent free-surface behavior due to wave-induced separation dominated the flow fields in the highest Froude number condition. From the instantaneous velocity fields, Time-mean velocity, turbulence kinetic energy, and flow structure derived by proper orthogonal decomposition (POD) were analyzed. As the free-surface effect, development of retarded wake, free-surface waves, and wave-induced separation were mainly observed.

Concerning the problems of difficult segmentation and large computation in calculating the Left Ventricular (LV) volume using tagged cardiac magnetic resonance image,a new method of calculating the LV volume was proposed. First an advanced snake model based on the gradient vector field (GVF-snake) was used to segment the contour of the left ventricular and an initial snake line of the contour was got,then optical flow method was used to track the contour of the LV in the successive images. Finally,the Simpson method was applied to calculate the volume of the LV. The comparison of the result by this method with the result got manually shows that the combination of the GVF-snake model and optical flow method is feasible to calculate the volume of the LV.

In-cylinder flow characteristics play a significant role in the fuel–air mixing process of Spark-Ignition Direct-Injection (SIDI) engines. Typically, planar Particle Image Velocimetry (PIV) is used to measure a representative velocity field sectioning through the center plane of the engine cylinder. However, a single flow field offers very limited perspective regarding the Three-Dimensional nature of the flow fields. Since the in-cylinder flow is stochastically complex, large datasets of flow field measurements along multiple planes are needed to provide a complete panoramic understanding of the flow dynamics. In this study, a high-speed PIV is applied to measure the crank-angle resolved flow fields inside a single-cylinder four-valve optical SIDI engine. Five flow fields along different tumble planes are captured. These five planes include two orthogonal planes cutting through the spark plug tip, two parallel planes sectioning through middle point of the intake and exhaust valves, and one plane through the centers of two intake valves. In addition, numerical computations are carried out with Large-Eddy Simulation (LES) model in CONVERGE. With the guidance from multi-plane PIV measurements, a novel validation approach is proposed in this study. The quantitative analysis and comparison between LES simulations and PIV experiments are divided in terms of global and local comparison indices. The global comparison indices provide a quantitative single value to quickly check the overall similarity of velocity directions and magnitudes between PIV and LES results of a specific individual plane. The local comparison indices further evaluate the similarity between the flow fields of LES and PIV point by point to identify any dissimilar regions and vortex features, which are likely to indicate the complex flow structures at low-speed regions. In summary, not only can the combined data analysis approach provide a reliable way for LES model validations, it can also reveal the physical quantifications of the complex in-cylinder flow characteristics.

Towards computer-assisted neurosurgery, scene understanding algorithms for microscope video data are required. Previous work utilizes optical flow to extract spatiotemporal context from neurosurgical video sequences. However, to select an appropriate optical flow method, we need to analyze which algorithm yields the highest accuracy for the neurosurgical domain. Currently, there are no benchmark datasets available for neurosurgery. In our work, we present an approach to generate synthetic data for optical flow evaluation on the neurosurgical domain. We simulate image sequences and thereby take into account domainspecific visual conditions such as surgical instrument motion. Then, we evaluate two optical flow algorithms, Farneback and PWC-Net, on our synthetic data. Qualitative and quantitative assessments confirm that our data can be used to evaluate optical flow for the neurosurgical domain. Future work will concentrate on extending the method by modeling additional effects in neurosurgery such as elastic background motion.

Motion and structure estimation are elementary problems of computer vision. These are active areas of research, even though the first methods were proposed several decades ago. We develop new approaches for motion and structure estimation for autonomous driving. An autonomous vehicle requires an accurate model of its environment, wrong decisions made by an autonomous car can have severe consequences. We assume the monocular setup, where only a single camera is mounted on the car. Outdoor traffic sequences are challenging for optical flow estimation. The high speed of the car causes large displacements in the optical flow field, the lighting conditions are unstable and there can be strong distortions due to reflections and difficult weather conditions. We propose new discrete methods, which determine optical flow as optimal configuration of probabilistic graphical models. The first approach selects sparse locations in the reference frame, and matches them across the second image. The best correspondences, which match constraints from a multiple view configuration, are considered motion vectors in a graphical model. In a second approach, we solve for dense optical flow by approximating the original infeasible graphical model with a sequence of reduced models. The monocular configuration poses challenges to the estimation of scene structure, camera positions and scene parameters need to be estimated jointly. The geometry of multiple views creates blind spots in the images, and adds a scale ambiguity, which both to not exist in a setup with multiple cameras. We propose two methods for structure estimation. The first approach determines the energy optimal camera track, given optical flow and depth observations. A further approach estimates camera motion and a piecewise planar scene description jointly from a single optical flow field. The scene description contains depth and plane normal information. We evaluate our approaches for motion and structure estimation on different real world and rendered datasets. In addition to evaluation on publicly available evaluation data, we evaluate on a new rendered dataset with ground truth plane normals.

Optical flow is a powerful image processing tool for measuring motion in digital images. The optical flow algorithm provides an estimate of the velocity vector at every pixel from a pair of successive images. Here we present an application of this method to images of coronal mass ejections (CMEs). The technique is first tested and validated on a simulated CME. It is then applied to several CMEs observed with the LASCO C2 coronagraph to derive their velocity fields. The resulting velocity measurements allow us to visualize the evolution of the CME plasma and to separate the bulk velocity from the expansion velocity of a given CME. To our knowledge, this is the first time that such information has been extracted from CME observations. We discuss the limitations and accuracy of our optical flow method and propose further improvements.

Quantitative image analysis and measurement of flow fields in convective heat transfer has great importance for the optimum energy consumption problems. In natural and forced convection phenomena of fluids, the complexity of flow field prevents us from detailed three dimensional (3D) experimental analyses of steady/unsteady dynamics in fluids. These flow fields have locally different density and temperature values and yet to be observed quantitatively. Recent development of the Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) techniques lead us to the quantitative investigation of flow fields in experimental researches. On the other hand, in image measurements density and temperature distributions have been grasped only in two-dimensions (2D). These qualitative image analyses of flow fields were obtained by using classical flow visualizing techniques, such as shadowgraph and color schlieren method. This paper describes the quantitative measurement of convective flow field using our originally proposed color striped background oriented schlieren (CSBOS) method. The obtained measured image data is used for CT reconstruction and 3D temperature gradient distributions.

This paper describes a resource-efficient vision system for the mini-robot “Khepera”. It is implemented to perform a fundamental approach of robotic navigation, which is obstacle detection by using an optical flow algorithm. This is inspired by visual perception of insects. The optical flow field is evaluated by implementing the sum of absolute differences (SAD) operation of block matching while the mobile mini-robot moves on texture plane. The SAD is realized in an additional processing module based on a Field Programmable Gate Array (FPGA) solution which can perform various tasks in parallel; for example, image processing, camera control and symbolic protocol transmission to the mini-robot. As a result, the Khepera equipped with 2D camera and the additional module is able to autonomously move avoiding collisions with obstacles, called ego-motion. The 30 × 16 SAD operation is processed by computing 16 pairs of pixels in each operation, which accomplishes the real-time approach. The proposed implementation reduces processing time and power consumption.

Two-step phase extraction and random phase shift estimation in phase-shifting profilometry based on least-squared optical flow method

This paper investigates the application of proper orthogonal decomposition (POD) for data obtained from visualizations. Using the POD method, the flow field behind one and two cylinders in a staggered configuration was analyzed. The data processed by this method were obtained from experimental measurements of flow fields using the particle image velocimetry (PIV) method and visualization. The dominant frequencies of the flow pattern from these data were compared using constant temperature anemometry (CTA) measurements. Attention was mainly focused on the flow at three Reynolds numbers: 500, 1200, and 2500. Velocity and vortex fields were created from PIV measurements in the wind tunnel for Re = 500, and video images of flow fields were obtained from dye visualizations in the hydrodynamic tunnel. The components of velocity, vorticity (both of PIV), and change in grayscale (from visualization) were used as input data for POD analysis. A methodology for data processing from visualizations was developed for subsequent analysis using the POD method. A new technique has been found to identify structures in the wake of the cylinders in a staggered configuration by analyzing POD based on various types of input data. The measured fields of dominant frequencies from the CTA and a thorough analysis of the POD modes and their relative energy values for each type of data made it possible to identify the structures and mechanisms that occur in the wake of cylinders. This analysis facilitated a better understanding of the importance of these structures and mechanisms, which can then be used to control the flow behind the cylinders.Graphic abstract