Cyclotella identification method and apparatus

Abstract

The embodiment of the invention discloses a cyclotella identification method and apparatus. The method comprises the steps of performing slice processing on a high-magnification microscopic image of an original water sample containing a to-be-tested alga to obtain a square identification image with a cyclotella shape as an internally tangent circle; performing polar coordinate transformation on the identification image, and extracting multiple identification characteristics of the image after the polar coordinate transformation; and inputting the extracted multiple identification characteristics to a decision-making tree model constructed in advance, and determining a category of the to-be-tested alga according to a decision-making tree model output result, wherein the decision-making treemodel performs classification using a C4.5 decision tree algorithm according to characteristics of a template cyclotella image and an information gain ratio of each training sample characteristics ina training sample image set, and performs identification on the to-be-tested alga according to a classification result. According to the technical scheme of the invention, that incision cannot be formed on an alga image under a complex background according to the prior art for identification is effectively solved, and the identification accuracy rate and efficiency on the alga are improved.

Description

Technical field [0001] The embodiment of the present invention relates to the technical field of image processing, and in particular to a method and device for identifying Cyclotella. Background technique [0002] Algae are commonly found in rivers, lakes and seas, wet soil, desert sand, hot springs, and even ice and snow. They are widely distributed on the earth and are closely related to the living environment of humans. They are used for fishery, aquaculture, medicine, food, agriculture and environmental protection. The fields have extremely important significance. The type and quantity of algae cells in a water body are important index parameters for monitoring water pollutants and judging water body quality. [0003] In the prior art, the classification and identification of algae in water bodies is generally carried out manually, which not only highly relies on subjective judgments such as the experience of the operator, and is time-consuming and laborious, but also cannot a...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, the algae in the water body are generally classified and identified manually, which not only highly depends on the operator's

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