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Wavelet neural network three-dimensional model classification method based on cloud model

A technology of wavelet neural network and three-dimensional model, which is applied in the direction of biological neural network model, neural learning method, character and pattern recognition, etc., and can solve problems such as poor algorithm matching effect

Active Publication Date: 2019-12-27
HARBIN UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

However, there are some shortcomings and deficiencies in the traditional algorithm
When the 3D model is complex, the extracted B-Rep data scale is large, and the matching effect of the algorithm will be very poor.

Method used

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  • Wavelet neural network three-dimensional model classification method based on cloud model
  • Wavelet neural network three-dimensional model classification method based on cloud model
  • Wavelet neural network three-dimensional model classification method based on cloud model

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Embodiment Construction

[0099] In order to clearly and completely describe the technical solutions in the embodiments of the present invention, the present invention will be further described in detail below in conjunction with the drawings in the embodiments.

[0100] This paper uses the 3D model data in the PSB model library of Princelington University for experimental verification.

[0101] The present invention implements the flow chart of the wavelet neural network three-dimensional model classification method based on the cloud model, as figure 1 shown, including the following steps.

[0102] Step 1 The geometric feature extraction process of the 3D model is as follows:

[0103] Three-dimensional model: take the model numbered m391 in the PSB model database of Princelington University as an example, the model is as follows Figure 4 .

[0104] Step 1-1 is based on the model file to calculate the shape feature D1 of the 3D model (the distance from the center of mass to a random point on the s...

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Abstract

The invention relates to a wavelet neural network three-dimensional model classification method based on a cloud model. The method comprises the following steps: firstly, performing feature extractionon a three-dimensional model, and performing frequency-depth traversal dimensionality reduction on shape features; converting the features of the three-dimensional model into qualitative concepts (model cloud features) represented by the cloud model by using the cloud model; and finally, training a wavelet neural network by using the cloud model features and the model categories of the three-dimensional model; inputting the cloud features of the three-dimensional model into a trained wavelet neural network model, and performing classification. The invention provides a more accurate and efficient three-dimensional model classification method, and the classification effect of the three-dimensional model is improved.

Description

Technical field: [0001] The invention relates to a wavelet neural network three-dimensional model classification method based on a cloud model, and the method has good application in the field of three-dimensional model classification. Background technique: [0002] With the continuous development of 3D modeling technology and computer vision, 3D model classification has attracted the attention of many scholars. Three-dimensional model classification has important applications in industry and engineering, and its classification effect is closely related to actual production. [0003] In 3D model classification, there are some common methods, such as: extracting B-Rep boundary features, constructing an undirected graph based on boundary features, and using subgraph matching algorithms to classify 3D models. However, there are some shortcomings and deficiencies in the traditional algorithm. When the 3D model is complex, the extracted B-Rep data is large in size, and the matc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/24G06F18/214Y02P90/30
Inventor 高雪瑶李佳伟张春祥
Owner HARBIN UNIV OF SCI & TECH