The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but stil...The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program.展开更多
为更好促成建筑信息模型(building information modeling,BIM)技术在实际工程中的有效应用,需要深入了解利益相关方在BIM取舍时对何种决策更加支持,以及何种风险因素更可能左右其决策.通过分析施工、环境、技术、合同4种风险场景行为,...为更好促成建筑信息模型(building information modeling,BIM)技术在实际工程中的有效应用,需要深入了解利益相关方在BIM取舍时对何种决策更加支持,以及何种风险因素更可能左右其决策.通过分析施工、环境、技术、合同4种风险场景行为,构建业主方、设计方和施工方三方演化博弈模型,并根据这些风险的影响程度分别调查三方主体,得到特征属性差别大的高维数据集,借助机器学习中的主成分分析(principal component analysis,PCA)方法,对高维数据集进行降维,并在实际工程场景下进行仿真模拟,得到了实现三方利益最大化的最优稳定策略及风险控制措施.结果表明,设计方在策略选择时最易受到风险因素的影响,但仍旧会积极地应用BIM技术,承包方会考虑BIM设计与实际施工存在出入,更倾向于减少对BIM技术的依赖,业主方选择对BIM应用中的行为进行直接干预,则可以促进设计方和承包方的合作,最大限度地实现项目利益最大化和风险的有效控制.展开更多
Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. ...Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.展开更多
Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has b...Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.展开更多
文摘The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program.
文摘为更好促成建筑信息模型(building information modeling,BIM)技术在实际工程中的有效应用,需要深入了解利益相关方在BIM取舍时对何种决策更加支持,以及何种风险因素更可能左右其决策.通过分析施工、环境、技术、合同4种风险场景行为,构建业主方、设计方和施工方三方演化博弈模型,并根据这些风险的影响程度分别调查三方主体,得到特征属性差别大的高维数据集,借助机器学习中的主成分分析(principal component analysis,PCA)方法,对高维数据集进行降维,并在实际工程场景下进行仿真模拟,得到了实现三方利益最大化的最优稳定策略及风险控制措施.结果表明,设计方在策略选择时最易受到风险因素的影响,但仍旧会积极地应用BIM技术,承包方会考虑BIM设计与实际施工存在出入,更倾向于减少对BIM技术的依赖,业主方选择对BIM应用中的行为进行直接干预,则可以促进设计方和承包方的合作,最大限度地实现项目利益最大化和风险的有效控制.
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.
基金The National Science Foundation for Young Scientists of China under contract No.41306193the National Special Research Fund for Non-Profit Marine Sector of China under contract No.201105016the ESA-MOST Dragon 3 Cooperation Programme under contract No.10501
文摘Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.
