Web online species recognition method based on blade complete and partial two-value characteristics

A technology of local binary features and identification methods, applied in the field of plant identification, can solve problems such as large cost ratio, great difference, and failure to popularize application, and achieve the effects of system design specifications, good recognition effect, and good robustness.

Inactive Publication Date: 2014-05-28
TONGJI UNIV
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Problems solved by technology

[0004] After investigation, it is found that (1) the above recognition methods are mostly based on the shape characteristics of the blades, and the shape sometimes varies greatly, resulting in a large recognition error. Texture features can overcome this defect very well and the application of texture recognition still very little
(2) Traditional identification is based on

Method used

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Embodiment Construction

[0029] The present invention will be described in detail below in conjunction with specific embodiments. In this embodiment, the technology of the present invention is used as Liti, and detailed implementation and specific operation process are given, but the scope of protection of the present invention is not limited to the following embodiments.

[0030] A Web online species identification method based on complete local binary features of leaves. This method is applied to the interactive system of B / S architecture. Implemented under CI (CodeIgniter) framework. The Web server needs to install and configure the Web online identification website, and configure the blade information database at the same time. The identification method specifically includes the following steps:

[0031] 1) Upload the plant leaf image to the Web server through the browser, and perform interactive operations in the browser, such as image size adjustment, operator parameter setting, etc., and prov...

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Abstract

The invention relates to a Web online species recognition method based on blade complete and partial two-value characteristics. The method includes the following steps of firstly, uploading blade images and interaction operation information to a Web server by means of a browser through an uploading interface; secondly, obtaining the blade images through the Web server, and conducting image preprocessing on the blade images; thirdly, extracting new complete and partial two-value textural features of the blade images in a new complete and partial two-value mode; fourthly, enabling the extracted new complete and partial two-value textural characteristics to serve as input of an SVM classifier, recognizing the blade images, and obtaining the serial numbers of species classifications corresponding to the blade images; fifthly, feeding the serial numbers of the species classifications and corresponding species information back to the browser through the Web server. Compared with the prior art, the method has the advantages of being high in accuracy, convenient and rapid to implement and the like.

Description

technical field [0001] The invention relates to a plant identification method, in particular to a Web online species identification method based on complete local binary features of leaves. Background technique [0002] Plant classification is to identify, classify, and name various plants by comparing various species, so as to facilitate people's understanding and understanding of plants. Traditional plant taxonomy research generally requires manual measurement and data acquisition of collected specimens, comprehensive plant external characteristics, and classification based on people's experience and common sense. This method has a huge workload and is highly subjective, leading to the possibility of identification errors. big sex. Similarly, there are about 250,000 species of flowering plants recorded by humans on the earth. With such a huge variety of plants, it is impossible for only one or two botanists to know the names of all species. Great difficulty came. [000...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 黄德双魏海彬赵森
Owner TONGJI UNIV
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