Knuckle Recognition Method Based on Infinite Dirichlet Process Mixture Model

A hybrid model and recognition method technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as the difference between model assumptions and observed data distribution, achieve stable and reliable detection results, reduce physical burden, and eliminate clumsy and insensitive operations. Effect

Inactive Publication Date: 2019-10-25
XIAN UNIV OF TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a knuckle recognition method based on the infinite Dirichlet process mixture model, which solves the problem of obvious differences between model assumptions and observed data distributions in the prior art

Method used

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  • Knuckle Recognition Method Based on Infinite Dirichlet Process Mixture Model
  • Knuckle Recognition Method Based on Infinite Dirichlet Process Mixture Model
  • Knuckle Recognition Method Based on Infinite Dirichlet Process Mixture Model

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Embodiment Construction

[0044] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0045] The present invention is based on the knuckle recognition method of the infinite Dirichlet process mixture model, and specifically follows the following steps:

[0046] Step 1: On the basis of local Markov hypothesis, transform the learning problem of conditional random measure into a random clustering learning problem;

[0047] According to the extraction of the image offset feature, the likelihood of the test image A to the random image gray distribution model is expressed as:

[0048]

[0049] among them Is the approximate form of the offset measure, It is the fusion structure between different offset set models under different offset parameters, Is the probability measure of the high-level shift set, Is a measure of the probability of the middle shift set.

[0050] According to the image gray position data extracted from the result of non-parametric de...

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Abstract

The invention discloses a knuckle recognition method based on an infinite Dirichlet process mixed model, which is carried out according to the following steps: step 1, on the basis of the local Markov assumption, the learning problem of conditional random measure is transformed into a random clustering learning problem; step 2. Use the infinite Dirichlet process mixture model to describe the probability density, and express the number of clusters as a random state; step 3, use the Gibbs sampling method to iteratively learn the density structure in the form of hierarchical probability; step 4, based on the Dirichlet process mixture model The collapsed Gibbs sampling algorithm DPMM uses the sample set for model training and learning, and uses a fixed threshold to recognize the knuckles of the hand image. The invention refines the description of the biological structure of the hand, the detection result is stable and reliable, and the calculation efficiency is high.

Description

Technical field [0001] The invention belongs to the technical field of intelligent manufacturing, and specifically relates to a knuckle recognition method based on an infinite Dirichlet process hybrid model. Background technique [0002] In the intelligent manufacturing system, the development of detection technology with a high degree of intelligence and strong environmental adaptability is of great significance to enhancing the flexibility of the manufacturing system, improving production efficiency and product quality. The human-computer interaction coordinated assembly technology based on machine vision uses the human assembly posture obtained by image analysis as the input information of the assembly robot task planning, and realizes efficient and flexible assembly through human-machine collaboration. The biological structure of the hand image and its association contain the overall information of the hand assembly posture, and the detection of the image features correspondi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/113G06V40/28G06F18/23
Inventor 杨世强弓逯琦柳培蕾李小莉杨江涛李德信
Owner XIAN UNIV OF TECH
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