Floc detection method combining three-frame differential higher-order statistics (HOS) with OTSU algorithm

A technology of high-order statistics and three-frame difference, which is applied in computing, image analysis, image data processing, etc., to achieve good results, complete extraction, and accurate real-time extraction

Active Publication Date: 2014-03-12
EAST CHINA JIAOTONG UNIVERSITY
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Problems solved by technology

[0010] The purpose of the present invention is to address the shortcomings of the three-frame difference method and the problems of noise influence and the current threshold selection method. The present invention proposes a method based on three-three frame difference high-order statistics (HOS) and particle swarm optimization enhanced Otsu method (OTSU) floc target detection method

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  • Floc detection method combining three-frame differential higher-order statistics (HOS) with OTSU algorithm
  • Floc detection method combining three-frame differential higher-order statistics (HOS) with OTSU algorithm
  • Floc detection method combining three-frame differential higher-order statistics (HOS) with OTSU algorithm

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specific Embodiment approach

[0041] According to the step of the inventive method, the specific embodiment of the present invention is as follows:

[0042] The first step: according to figure 1As shown, put the experimental equipment. Install sensors in the water at the end of the flocculation tank; make the water flow through the sampling window horizontally and slowly, and continuously collect the water flow (floc) images of the sampling window through the industrial camera, and set the sampling time interval according to actual needs in the experiment. The time interval Tsampe used in this experiment is 1s (in 1s, the number of flocs in the observation form is very representative). The number of particles is about 100, which can be used as a sample for image processing.

[0043] The second step: three-frame difference and noise processing. Respectively for the previous frame f of the captured image t-1 (x,y), the current frame f t (x,y), next frame f t+1 (x,y) for smoothing and denoising, and then...

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Abstract

Disclosed is a floc detection method combining three-frame differential higher-order statistics (HOS) with an OTSU algorithm. Since a commonly used floc tracking method is susceptible to noise, light and floc movement speed and the like at present and complete characteristics of a floc movement target are hard to extract, a floc target detection method based on combining the three-frame differential HOS with a particle swarm optimization enhancing OTSU algorithm is put forward. The floc detection method includes: subjecting consecutive three-frame images to differential calculation; calculating fourth-order moment pixel by pixel and comparing the fourth-order moment with threshold values; utilizing particle swarm optimization enhancing OTSU algorithm to acquire a best threshold value; utilizing the best threshold value to perform image binaryzation and image post-processing to acquire a relatively clear floc target finally. Therefore, a foundation of automatic subsequent floc analysis is laid. The floc detection method has the advantages of being accurate and rapid, capable of effectively extracting the floc target and applicable to effective extraction of the floc target in water treatment.

Description

technical field [0001] The invention relates to a floc detection method using three-frame differential high-order statistics combined with an OTSU algorithm, and belongs to the technical field of water treatment floc detection methods. Background technique [0002] With the continuous improvement of people's living standards, people's requirements for drinking water quality are also getting higher and higher. Conventional water treatment processes at home and abroad generally include several stages such as coagulation, sedimentation, filtration, and disinfection. During the process of tap water treatment, floc coagulation will occur, and the number, size, and sedimentation velocity of flocs are important parameters for judging the coagulation effect. Moving target detection is an important step in the quantitative analysis of floc images, and it is also the key to the automation of floc analysis, work stability, and accuracy of results. The images obtained by industrial cam...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/20
Inventor 谢昕李慧萍胡锋平
Owner EAST CHINA JIAOTONG UNIVERSITY
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