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The Research and Application of Image Measurement Method for River Rigime Width and Surface Flow Field in Yellow River Model

Author: YuHeng
Tutor: ZhaoJianJun
School: Henan University
Course: Control Theory and Control Engineering
Keywords: Yellow River Model River potential measurement Edge Detection PIV velocimetry
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 31
Quote: 0
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Abstract


This article is funded by the provincial Ministry to build the project \With the development of computer technology and image technology, the measuring method based on image analysis and related technologies in the river model test successfully achieved a non-contact measurement of the flow field, and has been the rapid development and wide application. There are a variety of entities Yellow River model river regime and flow rate measurement methods, but most can not be achieved fully automatic multi-point measurement, this paper on the basis of the experimental study of the solid model to establish a set of Yellow River model surface flow field measurement method with solid model of the river regime extraction, width measurement, and the measurement of the surface velocity field. This paper tightly around the image measurement technology entities river model experiments, described in detail the width of the river regime, the principle and method of surface flow field test. Pretreatment First potential image obtained through the camera model, r, using the method of filtering and threshold integrated processing, depending on the purpose of measurement. During river regime width measurement and comparison of various edge detection operator, the treatment effect is selected optimum operator, according to the characteristics of the image of the flow field, improved operator, proposed a new set of edge extraction methods, improved adaptive high and low thresholds of Canny edge detection algorithm and morphological connected domain segmentation wears combine effectively inhibit the interference edge automatic identification and extraction of the river model river regime, and then take advantage of the the tracer the calibration particles river Act to determine the river location, by calculating the number of pixels between the edge of the river regime, after the corresponding proportion of conversion to get the width of the river regime. Model river surface sow selected tracer particles in the process of conducting surface velocity measurements obtained by the camera sequence of images of the flow field, the image data is transmitted to the computer through the network, image preprocessing the PIV technology of surface flow rate measurement. Processing, according to the characteristics of the river model in order to get better results, reduce errors, this paper proposes a cross-correlation calculation is based on the adaptive analysis window method to determine the corresponding window size, and Hartley transform cross-correlation calculation instead of the traditional FFT transformation, on the measuring method of the PIV improved. Will improve the PIV technology applied to the surface of the model flow field velocity measurement error is small, flow rate information, and high accuracy. Finally, the post-processing of the measurement data, to culling and interpolation in the result of the flow velocity vector, and to further improve the accuracy of the velocity measurement. The this paper matlab7.0 has prepared include image pre-processing, measurement and the surface of the river regime flow rate measurement test system program of the three main functional modules, the application of experimental model test bench and specific entity model on the Yellow River, the measurement of success the width of the river regime as well as the surface velocity, and achieved good results. Also, the experimental results and error sources are analyzed and discussed the lack of applications and the development of image measurement techniques in the measurement of physical river model.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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