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Log Gaussian Cox場(chǎng)手部指節(jié)的圖像偏移特征學(xué)習(xí)與識(shí)別
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國(guó)家自然科學(xué)基金項(xiàng)目(51475365),、陜西省教育廳省級(jí)重點(diǎn)實(shí)驗(yàn)室科學(xué)研究計(jì)劃項(xiàng)目(12JS071)和陜西省教育廳科學(xué)研究計(jì)劃項(xiàng)目(2013JK1000)


Excursion Characteristic Learning and Recognition for Hand Image Knuckles Based on Log Gaussian Cox Field
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    摘要:

    針對(duì)手部指節(jié)圖像結(jié)構(gòu)特征模糊與建模困難的問(wèn)題,,以L(fǎng)og Gaussian Cox隨機(jī)場(chǎng)為圖像建模基礎(chǔ),,給出了隨機(jī)圖像上偏移特征的抽取與學(xué)習(xí)方法,實(shí)現(xiàn)了手部圖像中指節(jié)的識(shí)別,。在缺乏Cox過(guò)程圖像模型先驗(yàn)假設(shè)的條件下,,結(jié)合隨機(jī)圖像的水平集分解,得到了圖像偏移表示的逼近結(jié)果,。在圖像灰度分布非參數(shù)密度核估計(jì)基礎(chǔ)上,,利用非線(xiàn)性各向異性濾波對(duì)偏移特征進(jìn)行增強(qiáng),建立了偏移測(cè)度特征的Bayesian估計(jì),。提出了不同偏移參數(shù)下偏移特征的模型學(xué)習(xí)與融合算法,,獲得了指節(jié)圖像特征的融合表示,并在手部指節(jié)圖像數(shù)據(jù)庫(kù)中比較了不同分層偏移模型下的識(shí)別結(jié)果,,給出了批量識(shí)別ROC曲線(xiàn)統(tǒng)計(jì)規(guī)律,。結(jié)果表明,識(shí)別方法具有較為穩(wěn)定的正確分類(lèi)能力,,具有可行性,。

    Abstract:

    The effective description method for hand gesture is the most important in intelligent coordination assembly process based on human computer interaction. And effective hand finger knuckle detection is beneficial to the description of hand gesture.The structure characteristics of hand knuckles image are fuzzy and it is difficult to feature modeling. The extraction and learning method of excursion characteristic for hand knuckles image was presented and the hand knuckle was recognized by hand image based on Log Gaussian Cox random image model theory. The approximations of image excursion representation were given combined with level set decomposition of random image when the priori hypothesis was absented in Cox process image model. On the basis of nonparametric kernel estimation of image gray distribution, excursion characteristic was enhanced by nonlinear anisotropic filtering. And the Bayesian form of excursion measurement was established. The model learning and feature fusion algorithm on excursion characteristics with different excursion parameters was presented. And the features fusion representation of hand knuckle image was acquired. The hand knuckles image recognition results with many different hierarchical excursion data models were compared. The knuckle detection algorithm on hand image was presented. The ROC curves statisical law of hand knuckles detection with defferent models showed that the classification ablility of this method was correct and stable.The results also showed that the knuckle recognition ability of the model had some difference for different knuckle categories, and there were some differences in the deep distribution of image data between far knuckles and mid-knuckles. And the method was feasible.

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楊世強(qiáng),弓逯琦. Log Gaussian Cox場(chǎng)手部指節(jié)的圖像偏移特征學(xué)習(xí)與識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2017,48(1):353-360. YANG Shiqiang, GONG Luqi. Excursion Characteristic Learning and Recognition for Hand Image Knuckles Based on Log Gaussian Cox Field[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(1):353-360.

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  • 收稿日期:2016-08-09
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  • 在線(xiàn)發(fā)布日期: 2017-01-10
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