An Adaptive Soil Image Shadow Detection Method Based on FCM Algorithm

Abstract

A kind of self-adaptive soil image shadow detection method based on FCM algorithm provided by the present invention comprises: determine the cluster center of I component and L component of soil image; Build improved FCM algorithm optimization model: adopt Lagrangian multiplier method to Improve the FCM algorithm to optimize the model conversion; the optimized model after conversion is used for u ij , v i and lambda j Find the partial derivative and make the partial derivative equal to zero, solve for u ij and v i , according to the degree of membership u obtained in step S4 ij and the cluster centers v i Construct the membership degree matrix U and the cluster center matrix V, and construct the attraction weight matrix F; initialize the cluster center matrix V, the L component image and the I component image; find the cluster center with the smallest cluster center value, the cluster center It is the cluster center v_shadow of the shadow of the soil image, and extracts the data points belonging to the cluster center v_shadow, which are the shadow data points of the soil image; it can accurately detect the shadow in the soil image to ensure the detection accuracy, efficient.

Description

technical field [0001] The invention relates to an image processing method, in particular to an adaptive soil image shadow detection method based on an FCM algorithm. Background technique [0002] Soil natural fractures contain important soil type identification features, which are the most important feature identification points for soil soil type identification. The identification of soil natural fractures is often carried out through soil images. When acquiring soil images, soil natural fractures are due to There are uneven phenomena, which lead to shadows in the image. Therefore, it is necessary to detect the shadow of the soil image and remove the shadow in the later stage, so as to ensure the accuracy of soil type identification. In the prior art, for the shadow of the soil image The detection accuracy is low, and the algorithm process is complicated. [0003] Therefore, in order to solve the above technical problems, it is urgent to propose a new technical means. C...

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Problems solved by technology

[0002] Soil natural fractures contain important soil type identification features, which are the most important feature identification points for soil soil type identification. The identification of soil natural fractures is often carried out through soil images. When acquiring soil images, soil natural fractures are due to There are uneven phenomena, which lead to shadows in the image. Therefore, it is necessary to detect the shadow of the soil image and remove the shadow in the later stage, so as to ensure the accuracy of soil type identification. In the prior art, for the shadow of the soil image The detection accuracy is low, and the algorithm process is complicated

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