• Journal of the Korea Society of Computer and Information
• /
• v.20 no.11
• /
• pp.9-16
• /
• 2015

We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.2
• /
• pp.259-266
• /
• 1992

For CAD/CAPP integration, part information on not only geometry but also machining characteristics should be delivered and commonly used between designers and process planners. In this study, the machining features, as linking factors of the integration, are represented as the combination of functional features and atomic features and grouped into a hierarchical database. And the feature based modelling approach is used by generating information on the machining features in design stage. These features are drawn by analyzing real decision rules of process planners. The database using the machining features is built and used for application modules of process planning, operation planning and standard time estimation.

• Journal of Biomedical Engineering Research
• /
• v.15 no.2
• /
• pp.201-208
• /
• 1994

Many three-dimensional object modeling and display methods for computer graphics and computer vision have been developed. Recently, with the help of medical imaging devices such as computerized tomography, magnetic resonance image, etc., some of those object modeling and display methods have been widely used for capturing the shape, structure and other properties of real objects in many medical applications. In this paper, we propose the reconstruction and display method of the three-dimensional object from a series of the cross sectonal image. It is implemented by using the automatic threshold selection method and the contour following algorithm. The combination of curvature and distance, we select feature points. Those feature points are the candidates for the tiling method. As a results, it is proven that this proposed method is very effective and useful in the comprehension of the object's structure. Without the technician's responce, it can be automated.

• Journal of Information Processing Systems
• /
• v.10 no.4
• /
• pp.555-567
• /
• 2014

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

• Journal of Information Processing Systems
• /
• v.13 no.6
• /
• pp.1628-1639
• /
• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.349-354
• /
• 1996

The phonetic mastery of English has been considered next to impossible to many non-native speakers of English, including even some teachers of English. This paper takes issue with this phonetic problem of second language acquisition and proposes that combination of cognitive and physical approaches can help master English faster and more easily.

• Journal of the Speleological Society of Korea
• /
• no.78
• /
• pp.1-8
• /
• 2007

The combination of mechano-chemical activation (MCA)and Self-propagating High-temperature Synthesis (SHS) has widened the technical possibilities for both methods. For YBCO systems the investigation showed that a short-term MCA of initial powders before SHS leads to single-phase and ultra-fine products. A new technique for preparation ultra-fine high-temperature superconductors (HTS) of YBCO composition with a grain size d <1m is developed using combination of MCA and SHS. The specific feature of the technique is formation of the $YBa_2Cu_3O_7-$ crystalline lattice directly from an X-ray amorphous state arising as a result of mechanical activation of the original oxide mixture. The technique allows the stage of formation of any intermediate reaction products to be ruled out. X-ray and magnetic studies of ultra-fine high temperature superconductors are carried out. Dimension effects associated with the microstructure peculiarities are revealed. A considerable enhancement of inter-grain critical currents is found to take place in the ultra-fine samples.

• Journal of the Korea Society of Computer and Information
• /
• v.10 no.6 s.38
• /
• pp.1-8
• /
• 2005

Facial analysis is used in many applications like face recognition systems, human-computer interface through head movements or facial expressions, model based coding, or virtual reality. In all these applications a very precise extraction of facial feature points are necessary. In this paper we presents a method for automatic extraction of the facial features Points such as mouth corners, eye corners, eyebrow corners. First, face region is detected by AdaBoost-based object detection algorithm. Then a combination of three kinds of feature energy for facial features are computed; valley energy, intensity energy and edge energy. After feature area are detected by searching horizontal rectangles which has high feature energy. Finally, a corner detection algorithm is applied on the end region of each feature area. Because we integrate three feature energy and the suggested estimation method for valley energy and intensity energy are adaptive to the illumination change, the proposed feature extraction method is robust under various conditions.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.31-39
• /
• 2023

In this paper, we propose an Internal/External Knowledge Distillation (IEKD), which utilizes both external correlations between feature maps of heterogeneous models and internal correlations between feature maps of the same model for transferring knowledge from a teacher model to a student model. To achieve this, we transform feature maps into a sequence format and extract new feature maps suitable for knowledge distillation by considering internal and external correlations through a transformer. We can learn both internal and external correlations by distilling the extracted feature maps and improve the accuracy of the student model by utilizing the extracted feature maps with feature matching. To demonstrate the effectiveness of our proposed knowledge distillation method, we achieved 76.23% Top-1 image classification accuracy on the CIFAR-100 dataset with the "ResNet-32×4/VGG-8" teacher and student combination and outperformed the state-of-the-art KD methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.11
• /
• pp.2720-2736
• /
• 2013

Multi-view face detection has become an active area for research in the last few years. In this paper, a novel multi-view human face detection algorithm based on improved real Adaboost is presented. Real Adaboost algorithm is improved by weighted combination of weak classifiers and the approximately best combination coefficients are obtained. After that, we proved that the function of sample weight adjusting method and weak classifier training method is to guarantee the independence of weak classifiers. A coarse-to-fine hierarchical face detector combining the high efficiency of Haar feature with pose estimation phase based on our real Adaboost algorithm is proposed. This algorithm reduces training time cost greatly compared with classical real Adaboost algorithm. In addition, it speeds up strong classifier converging and reduces the number of weak classifiers. For frontal face detection, the experiments on MIT+CMU frontal face test set result a 96.4% correct rate with 528 false alarms; for multi-view face in real time test set result a 94.7 % correct rate. The experimental results verified the effectiveness of the proposed approach.