Computer Vision for Wave Flume Experiments

Computer Vision for Wave Flume Experiments

Óscar Ibáñez (University of A Coruña, Spain) and Juan Ramón Rabuñal Dopico (University of A Coruña, Spain)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch059
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Abstract

During the past several decades, a number of attempts have been made to contain oil slicks (or any surface contaminants) in the open sea by means of a floating barrier. Many of those attempts were not very successful especially in the presence of waves and currents. The relative capabilities of these booms have not been properly quantified for lack of standard analysis or testing procedure (Hudon, 1992). In this regard, more analysis and experimental programs to identify important boom effectiveness parameters are needed. To achieve the desirable performance of floating booms in the open sea, it is necessary to investigate the static and dynamic responses of individual boom sections under the action of waves; this kind of test is usually carried out in a wave flume, where open sea conditions can be reproduced at a scale. Traditional methods use capacitance or conductivity gauges (Hughes, 1993) to measure the waves. One of these gauges only provides the measurement at one point; further, it isn’t able to detect the interphase between two or more fluids, such as water and a hydrocarbon. An additional drawback of conventional wave gauges is their cost. Other experiments such as velocity measurements, sand concentration measurements, bed level measurements, breakwater’s behaviour, etc… and the set of traditional methods or instruments used in those experiments which goes from EMF, ADV for velocity measurements to pressure sensors, capacity wires, acoustic sensors, echo soundings for measuring wave height and sand concentration, are common used in wave flume experiments. All instruments have an associate error (Van Rijn, Grasmeijer & Ruessink, 2000), and an associate cost (most of them are too expensive for a lot of laboratories that can not afford pay those amount of money), certain limitations and some of them need a large term of calibration. This paper presents another possibility for wave flume experiments, computer vision, which used a cheap and affordable technology (common video cameras and pc’s), it is calibrated automatically (once we have developed the calibration task), is a non-intrusive technology and its potential uses could takes up all kind experiments developed in wave flumes. Are artificial vision’s programmers who can give computer vision systems all possibilities inside the visual field of a video camera. Most experiments conducted in wave flumes and new ones can be carried out programming computer vision systems. In fact, in this paper, a new kind of wave flume experiment is presented, a kind of experiment that without artificial vision technology it couldn’t be done.
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Background

Wave flume experiments are highly sensitive to whatever perturbation; therefore, the use of non-invasive measurement methodologies is mandatory if meaningful measures are desired. In fact, theoretical and experimental efforts whose results have been proposed in the literature have been mainly conducted focusing on the equilibrium conditions of the system (Niederoda and Dalton, 1982), (Kawata and Tsuchiya, 1988).

In contrast with most traditional methods used in wave flume experiments computer vision systems are non-invasive ones since the camera is situated outside the tank and in addition provide better accuracy than most traditional instruments.

The present work is part of a European Commission research project, “Advanced tools to protect the Galician and Northern Portuguese coast against oil spills at sea”, in which a number of measurements in a wave flume must be conducted, such as the instantaneous position of the water surface or the motions (Milgran, 1971) of a floating containment boom to achieve these objectives, a non-intrusive method is necessary (due to the presence of objects inside the tank) and the method has to be able to differentiate between at least two different fluids, with the oil slick in view.

Key Terms in this Chapter

Morphological Operators: (Haralick and Shapiro, 1992; Vernon, 1991) Mathematical morphology is a set-theoretical approach to multi-dimensional digital signal or image analysis, based on shape. The signals are locally compared with so-called structuring elements of arbitrary shape with a reference point.

Erosion: The basic effect of the operator on a binary image is to reduce the definition of the objects. The erosion in the point (x,y) is the minimum value of all the points situated under the window, which is defined by the structuring element ‘Y’ that travels around the image

Color Spaces: (Konstantinos & Anastasios, 2000) supply a method to specify, sort and handle colors. These representations match n-dimensional sorts of the color feelings (n-components vector). Colors are represented by means of points in these spaces. There are lots of colors spaces and all of them start from the same concept, the Tri-chromatic theory of primary colors, red, green and blue.

Videometrics: (Tsai, 1987) can loosely be defined as the use of imaging technology to perform precise and reliable measurements of the environment.

Harris Corner Detector: A popular interest point detector (Harris and Stephens, 1988) due to its strong invariance to (Schmid, Mohr, & Bauckhage, 2000): rotation, scale, illumination variation and image noise. The Harris corner detector is based on the local auto-correlation function of a signal; where the local auto-correlation function measures the local changes of the signal with patches shifted by a small amount in different directions.

Image Moments: (Hu, 1963; Mukundan and Ramakrishman, 1998) they are certain particular weighted averages (moments) of the image pixels’ intensities, or functions of those moments, usually chosen to have some attractive property or interpretation. They are useful to describe objects after segmentation. Simple properties of the image which are found via image moments include area (or total intensity), its centroid, and information about its orientation.

Dilation: The dilation of an image by a structuring element ‘Y’ is defined as the maximum value of all the pixels situated under the structuring element The basic effect of this morphological operator the operator on a binary image is to gradually enlarge the boundaries of regions of foreground pixels (i.e. white pixels, typically). Thus areas of foreground pixels grow in size while holes within those regions become smaller.

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