本文選題：手勢(shì)識(shí)別　切入點(diǎn)：高斯膚色模型　出處：《計(jì)算機(jī)工程與應(yīng)用》2017年06期

【摘要】：在手勢(shì)識(shí)別研究過(guò)程中,人工選取特征難以適應(yīng)手勢(shì)的多變性。提出了一種結(jié)合膚色模型和卷積神經(jīng)網(wǎng)絡(luò)的手勢(shì)識(shí)別方法,對(duì)采集的不同背景下的手勢(shì)圖像,首先用膚色高斯模型分割出手勢(shì)區(qū)域,然后采用卷積神經(jīng)網(wǎng)絡(luò)建立手勢(shì)的識(shí)別模型,該模型融合了手勢(shì)特征提取和分類過(guò)程,模擬視覺(jué)傳導(dǎo)和認(rèn)知,有效避免了人工特征提取的主觀性和局限性。識(shí)別模型以手勢(shì)區(qū)域的灰度信息為輸入,同時(shí)利用權(quán)值共享和池化等技術(shù)減少網(wǎng)絡(luò)權(quán)值個(gè)數(shù),降低了模型的復(fù)雜度。實(shí)驗(yàn)結(jié)果表明,卷積神經(jīng)網(wǎng)絡(luò)(CNN)方法能夠有效進(jìn)行特征學(xué)習(xí),在不同數(shù)據(jù)集下對(duì)手勢(shì)的平均識(shí)別率都達(dá)到95%以上,與傳統(tǒng)方法進(jìn)行對(duì)比實(shí)驗(yàn),表明該方法具有較高的識(shí)別率和實(shí)時(shí)性。
[Abstract]:In the process of gesture recognition, artificial feature selection is difficult to adapt to the variety of gestures. A combination of skin color model and convolutional neural network method of gesture recognition, gesture image acquisition under the background of different, firstly divided the gesture area with color Gauss model, then the model recognition gesture convolutional neural network. The model combines the feature extraction and classification process, simulation of visual conduction and cognition, effectively avoids the subjectivity and limitation of artificial feature extraction. Recognition model by gray information of the gesture area as input, using weight sharing and pooling technology to reduce the number of network weights, reduce the complexity of model experiment. The results show that the convolution neural network (CNN) method can effective learning characteristics in different data sets, the average for the gesture recognition rate of over 95%, and the traditional party The comparison experiment shows that the method has high recognition rate and real time.

【作者單位】： 昆明理工大學(xué)信息工程與自動(dòng)化學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(No.61263017) 云南省自然科學(xué)基金(No.2011FZ060,No.KKSY201303120)
【分類號(hào)】：TP391.41;TP183

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本文編號(hào)：1669099