This letter proposes an effective method for recognizing face images by combining two-Dimen- sional Principal Component Analysis (2DPCA) with IMage Euclidean Distance (IMED) method. The proposed method is comprised of...This letter proposes an effective method for recognizing face images by combining two-Dimen- sional Principal Component Analysis (2DPCA) with IMage Euclidean Distance (IMED) method. The proposed method is comprised of four main stages. The first stage uses the wavelet decomposition to extract low fre- quency subimages from original face images and omits the other three subimages. The second stage concerns the application of IMED to face images. In the third stage, 2DPCA is employed to extract the face features from the processed results in the second stage. Finally, Support Vector Machine (SVM) is applied to classify the extracted face features. Experimental results on the AR face image database show that the proposed method yields better recognition performance in comparison with the 2DPCA method that is not combined with IMED.展开更多
Feature recognition is a process of extracting machining features which has engineering meaning from solid model, and it is a key technology of CAD/CAPP/CAM integration. This paper presents an effective and efficient ...Feature recognition is a process of extracting machining features which has engineering meaning from solid model, and it is a key technology of CAD/CAPP/CAM integration. This paper presents an effective and efficient methodology of recognizing machining feature. In this approach, features are classified into two categories： pocket feature and predefined feature. Different feature type adopts its special hint and heuristic rule, and is helpful to recognize intersection feature. Feature classification optimizes search algorithm and shortens search scope dramatically. Meanwhile, extension and split algorithm is used to handle intersecting feature. Moreover, feature mapping based on machining knowledge is introduced to support downstream application better. Finally, case studies with complex intersecting features prove that the developed approach has stronger recognizing ability.展开更多
文摘This letter proposes an effective method for recognizing face images by combining two-Dimen- sional Principal Component Analysis (2DPCA) with IMage Euclidean Distance (IMED) method. The proposed method is comprised of four main stages. The first stage uses the wavelet decomposition to extract low fre- quency subimages from original face images and omits the other three subimages. The second stage concerns the application of IMED to face images. In the third stage, 2DPCA is employed to extract the face features from the processed results in the second stage. Finally, Support Vector Machine (SVM) is applied to classify the extracted face features. Experimental results on the AR face image database show that the proposed method yields better recognition performance in comparison with the 2DPCA method that is not combined with IMED.
文摘Feature recognition is a process of extracting machining features which has engineering meaning from solid model, and it is a key technology of CAD/CAPP/CAM integration. This paper presents an effective and efficient methodology of recognizing machining feature. In this approach, features are classified into two categories： pocket feature and predefined feature. Different feature type adopts its special hint and heuristic rule, and is helpful to recognize intersection feature. Feature classification optimizes search algorithm and shortens search scope dramatically. Meanwhile, extension and split algorithm is used to handle intersecting feature. Moreover, feature mapping based on machining knowledge is introduced to support downstream application better. Finally, case studies with complex intersecting features prove that the developed approach has stronger recognizing ability.
