An image processing method based on a Gaussian background modeling technology of mathematical morphology

Abstract

The invention discloses a method for carrying out image processing based on a Gaussian background modeling technology of mathematical morphology. Firstly, each new pixel value Xt on an image is collected; the new pixel values are compared with the current K model expressions till the distribution model matched with the new pixels is found; the weight of each mode is updated; the weight of each mode is normalized, if no mode is matched, the mode with the minimum weight is replaced, that is, the mean value of the modes is the current pixel value, the standard deviation is the initial large value, the weight is the small value, then all the modes are arranged in a descending order according to w / alpha 2, wherein the modes with the large weight and the small standard deviation are arranged inthe front. According to the invention, a format of image processing performed by a traditional Gaussian model and a mixed Gaussian model is eliminated; by means of mathematical morphology processing,the workload of calculation in the background updating process is effectively reduced, the real-time updating requirement is met, the data updating speed is increased, the number of parameter learningrates in a Gaussian model is greatly reduced, the working efficiency is improved, and the operation process is weakened.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image processing method based on Gaussian background modeling technology of mathematical morphology. Background technique [0002] Mathematical morphology is an image analysis subject based on lattice theory and topology, and is the basic theory of mathematical morphology image processing. The Gaussian model uses the Gaussian probability density function, that is, the normal distribution curve to accurately quantify things, and decomposes a thing into several models based on the Gaussian probability density function. Mixed Gaussian background modeling is a background representation method based on statistical information of pixel samples, using statistical information such as the probability density of a large number of sample values ​​of pixels in a long period of time, such as the number of modes, the mean and standard deviation of each mode to represent the backgro...

AI Technical Summary

Problems solved by technology

[0003] However, the traditional mixed Gaussian model must update the parameters of all Gaussian functions of all pixels in the image when obtaining a new video frame image, which requires a large amount of calculation and a complicated operation process. Therefore, it is necessary to use the traditional Gaussian background Improve the method of image processing by modeling, reduce its calculation process, simplify its calculation amount, achieve the purpose of strong practicability and improve efficiency

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