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Feature extraction method of three-dimensional point cloud under persistent homology

A three-dimensional point cloud and feature extraction technology, applied in image data processing, instrumentation, computing and other directions, can solve the problem of complex factors of feature description ability, and achieve the effect of wide applicability and strong robustness

Inactive Publication Date: 2019-11-12
TAIYUAN NORMAL UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Most of the existing 3D point cloud feature analysis is aimed at rigid objects with relatively simple structures, but the 3D point clouds in real life are varied, and the factors that affect their feature description ability are complex

Method used

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  • Feature extraction method of three-dimensional point cloud under persistent homology
  • Feature extraction method of three-dimensional point cloud under persistent homology
  • Feature extraction method of three-dimensional point cloud under persistent homology

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Embodiment Construction

[0028] refer to image 3 The flow chart of the present invention is further elaborated by taking the three-dimensional brain image feature analysis of Alzheimer's disease as an example, and the specific implementation method is as follows:

[0029] First, preprocess the three-dimensional image data of the brain of Alzheimer's disease. The preprocessing steps are as follows:

[0030] S1: Randomly generate three groups of data, each group includes K=40 subjects, and each subject has S=100 three-dimensional scanning images;

[0031] S2: M=90 brain regions are defined by the AAL90 brain atlas, and each brain region is regarded as a node;

[0032] After preprocessing, the following calculation steps are required:

[0033] Step 1: Observe the rank of the homology group of the three-dimensional brain image data after preprocessing, and use the connectivity based on the k-dimensional simplicial complex to distinguish the topological space. The k-th dimension Betti number is the rank...

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Abstract

The invention discloses a feature extraction method of a three-dimensional point cloud under persistent homology. According to the invention, a CCAC feature is added on the basis of the zeroth-dimensional betti number; subsequent evolution information of point cloud nested replication is further estimated; a novel integrated persistent feature IPF and a single-variable topological feature SIP which is obtained through coacervation of the IPF are provided; the betti number is expanded theoretically; the method can represent a spatial evolution process of nested replication more completely. Themethod can be used for measuring the topological structure change of the complex point cloud, has better statistical performance than the characteristics based on the graph theory, and can become a very effective method for extracting the potential imaging biomarker of the Alzheimer's disease. The invention provides a feature representation method which is wide in applicability and high in robustness, and can meet the requirements of different levels in practical application.

Description

technical field [0001] The invention relates to the technical field of feature extraction methods, in particular to a feature extraction method of a three-dimensional point cloud under persistent coherence. Background technique [0002] Most of the existing 3D point cloud feature analysis is aimed at rigid objects with relatively simple structures, but the 3D point cloud in real life is changing and varied, and the factors that affect its feature description ability are complex. Therefore, it is the focus and difficulty of 3D point cloud feature representation to study some feature representation methods with wide applicability and strong robustness to meet the needs of different levels in practical applications. Contents of the invention [0003] In order to solve the shortcomings and deficiencies of the existing technology, a feature extraction method of 3D point cloud under persistent coherence is provided to meet the needs of different levels in practical applications....

Claims

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Application Information

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IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10028G06T2207/30016
Inventor 阴桂梅况立群韩燮郭广行
Owner TAIYUAN NORMAL UNIV
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