Knuckle recognition method based on infinite Dirichlet process mixture model

A hybrid model and recognition method technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as the difference between model assumptions and observed data distribution, achieve stable and reliable detection results, high calculation efficiency, and eliminate clumsy operations insensitive effect

Inactive Publication Date: 2018-11-13
XIAN UNIV OF TECH
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Problems solved by technology

[0008] The purpose of the present invention is to provide a knuckle recognition method based on the infinite Dirichlet process mixtu

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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 in conjunction with the accompanying drawings and specific embodiments.

[0045] The present invention is based on the phalanx recognition method of infinite Dirichlet process mixture model, specifically carries out according to the following steps:

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

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

[0048]

[0049] in is the approximate form of the offset measure, is the fusion structure between different offset set models under different offset parameters, is the high-level offset set probability measure, is the probability measure of the mid-level offset set.

[0050] According to the image gray position data extracted from the re...

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Abstract

The invention discloses a knuckle recognition method based on an infinite dirichlet process mixture model. The method is implemented by the following steps of 1, on the basis of a local Markov hypothesis, converting a learning problem of condition random measure into a random clustering learning problem; 2, describing probability density by using the infinite Dirichlet process mixture model, and expressing the number of clusters as a random state; 3, by utilizing a Gibbs sampling method, carrying out iterative learning on a density structure in a layered probability form; and 4, based on a collapse Gibbs sampling algorithm of the Dirichlet process mixture model (DPMM), performing model training learning by applying a sample set, and recognizing knuckles of a hand image by adopting a fixedthreshold value. According to the method, the description of the biological structure of a hand is refined; a 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 in particular relates to a knuckle recognition method based on an infinite Dirichlet process mixture 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 for enhancing the flexibility of the manufacturing system, improving production efficiency and product quality. Human-computer interaction coordination assembly technology based on machine vision uses the human body assembly posture obtained by image analysis as the input information for assembly robot task planning, and realizes efficient and flexible assembly through human-computer cooperation. The biological structure of the hand image and its association contain the overall information of the hand assembly pose, and the detection of the image features correspo...

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Application Information

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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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