计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2015年
4期
199-204
,共6页
计算机视觉%人体姿态估计%外观模型%特征提取%纹理特征%加权Haar型局部二值模式特征
計算機視覺%人體姿態估計%外觀模型%特徵提取%紋理特徵%加權Haar型跼部二值模式特徵
계산궤시각%인체자태고계%외관모형%특정제취%문리특정%가권Haar형국부이치모식특정
computer vision%human pose estimation%appearance model%feature extract%texture feature%Weighted Haar characteristics Local Binary Pattern( WHLBP)
基于人体部件的树形模型表达直观且计算高效，被广泛应用在人体姿态估计中。然而模型本身在部件特征表达上的不足限制了姿态估计结果的准确度，为此，提出一种基于图结构模型和新型纹理特征的人体姿态估计算法。采用改进后的外观模型，从训练集中获得部件位置的先验知识，联系相邻部件之间的关系，并将其应用于测试图像的外观模型建模阶段。应用Haar型局部二值模式( HLBP )纹理特征，提取部件的纹理信息，对图像进行分块处理，并为每一块赋予不同的权重。实验结果表明，带权重的HLBP特征能更有效地提取部件的纹理特征，与HLBP特征、归一化HLBP特征和颜色特征相比能获得更高的准确度。
基于人體部件的樹形模型錶達直觀且計算高效，被廣汎應用在人體姿態估計中。然而模型本身在部件特徵錶達上的不足限製瞭姿態估計結果的準確度，為此，提齣一種基于圖結構模型和新型紋理特徵的人體姿態估計算法。採用改進後的外觀模型，從訓練集中穫得部件位置的先驗知識，聯繫相鄰部件之間的關繫，併將其應用于測試圖像的外觀模型建模階段。應用Haar型跼部二值模式( HLBP )紋理特徵，提取部件的紋理信息，對圖像進行分塊處理，併為每一塊賦予不同的權重。實驗結果錶明，帶權重的HLBP特徵能更有效地提取部件的紋理特徵，與HLBP特徵、歸一化HLBP特徵和顏色特徵相比能穫得更高的準確度。
기우인체부건적수형모형표체직관차계산고효，피엄범응용재인체자태고계중。연이모형본신재부건특정표체상적불족한제료자태고계결과적준학도，위차，제출일충기우도결구모형화신형문리특정적인체자태고계산법。채용개진후적외관모형，종훈련집중획득부건위치적선험지식，련계상린부건지간적관계，병장기응용우측시도상적외관모형건모계단。응용Haar형국부이치모식( HLBP )문리특정，제취부건적문리신식，대도상진행분괴처리，병위매일괴부여불동적권중。실험결과표명，대권중적HLBP특정능경유효지제취부건적문리특정，여HLBP특정、귀일화HLBP특정화안색특정상비능획득경고적준학도。
Tree structured model based on body parts is widely used in human pose estimation filed due to its intuitive expression and effective calculation. In order to address the problem of low accuracy of pose estimation caused by insufficient feature expression of body part models,a pose estimation method based on pictorial structure and novel text feature is proposed. Better appearance model is adopted,prior and latent relationships of different body parts are learned from annotated images then help in estimating better appearance models on test images. Haar characteristics Local Binary Pattern( HLBP) text feature is used to extract the text information of body parts. Furthermore,an image is block processed and different weight is assigned to different block. Experimental results show that when compared with HLBP, normed HLBP and color feature,Weighted HLBP( WHLBP) captures text feature more effectively and gains higher accuracy.
