• Korean Architects
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• s.561
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• pp.140-145
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• 2016

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.7-12
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• 2009

In this paper, we propose a directional feature extraction method for off-line handwritten numerals by using the morphological operations. Direction features are obtained from four directional line images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral lines. Conventional method for extracting directional features uses Kirsch masks which generate edge-shaped double line images for each direction, whereas our method uses directional erosion operations and generate single line images for each direction. To apply these directional erosion operations to the numeral image, preprocessing steps such as thinning and dilation are required, but resultant directional lines are more similar to numeral lines themselves. Our four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. For obtaining the higher recognition rates of the handwrittern numerals, we use the multiple feature which is comprised of our proposed feature and the conventional features of a kirsch directional feature and a concavity feature. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the CENPARMI numeral database of Concordia University, we have achieved a recognition rate of 98.35%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.804-814
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• 2009

In this paper, a novel system for avatar motion controlling by tracking face is presented. The system is composed of three main parts: firstly, LCS (Local Cluster Searching) method based face feature detection algorithm, secondly, HMM based feature points recognition algorithm, and finally, avatar controlling and animation generation algorithm. In LCS method, face region can be divided into many small piece regions in horizontal and vertical direction. Then the method will judge each cross point that if it is an object point, edge point or the background point. The HMM method will distinguish the mouth, eyes, nose etc. from these feature points. Based on the detected facial feature points, the 3D avatar is controlled by two ways: avatar orientation and animation, the avatar orientation controlling information can be acquired by analyzing facial geometric information; avatar animation can be generated from the face feature points smoothly. And finally for evaluating performance of the developed system, we implement the system on Window XP OS, the results show that the system can have an excellent performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.822-826
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.5
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• pp.15-23
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• 2003

A feature-based inspection planning system is proposed in this research to develop more efficient measuring methodology for the OMM(On-Machine Measurement) or CMM(coordinate Measuring Machine) for complicated workpiece having many primitive form features. This paper is proposed solution that optimum inspection sequence of the objective features. The sequences are determined by analyzing the feature information such as the nearest relationship and the possible probe-approach direction(PAD) of the features, and forming feature groups. A series of heuristic rules are developed to accomplish it. Also, each feature is decomposed into its constituent geometric elements for inspection process, and then the number of sampling points, location of the measuring points, optimum probing path are determined.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• Journal of Korea Multimedia Society
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• v.21 no.7
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• pp.755-763
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• 2018

In this paper, for recognize a night pedestrian from an infrared video, a new method differentiated from the existing feature vector is proposed and experimented. The new approach focuses on the shape feature vector of the structure and shape of the pedestrian image divided by the human body seven split ratio. The pedestrian images are divided into 7 square blocks from the still image of the preprocessing process. And to reduce the dimension, the square block is converted into a mosaic block. The scalar and direction of the shape feature vector is calculated by the brightness and position of the element in the mosaic. For practicality of infrared video system, the proposed method simplifies the data to be processed by reducing the amount of data in the preprocessing in order to continuously batch process the entire system in real time. Through the experiments, we verified the validity of the proposed shape feature vector. In comparison to the existing method, we propose a new shape feature vector generation method as the feature vector for night pedestrian recognition.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.38.1-38.1
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• 2008

Tides caused by the Galactic gravitational field affect the current dynamical structure of globular clusters in the Galaxy. Indeed, the observed feature of tidal tails stretching beyond globular clusters' tidal radii provides a key information of interaction with the gravitational field of the Galaxy and kinematical orbit of the clusters, which can be an evidence of the merging scenario of the Galaxy formation and evolution. To find such a tidal feature, we have studied spatial density distribution of stars around five globular clusters in the Galactic halo and one cluster in the Galactic bulge, for which we have used wide-field deep photometric data of gri and JHK bands obtained from the MegaCam and WIRCam of the CFHT. Applying the statistical contrast filtering of field stars in the color-magnitude plane of detected stars around five halo clusters, we have found features of tidal tails for four clusters M53, M15, NGC 5053, and NGC 5466. The detected over-density tidal features are well aligned with the cluster's orbits and stretched into the direction of the Galactic center. Statistical analysis indicate that these tidal tails are believed to be cluster stars that have escaped due to the tidal effects to the clusters. A similar tidal feature to that of halo clusters is also detected for the bulge cluster NGC 6626, while the over-density feature seems to be extended into the Galactic plane rather than into the orbital direction and the Galactic center. Conclusively, our result adds further observational evidence of the merging scenario of the Galaxy formation and evolution.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.585-589
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• 2002

In this paper, hierarchical retrieval system based on efficient feature extraction is proposed. In order to retrieval the image with robustness for geometrical transformation such as translation, scaling, and rotation. After performing the 2-level wavelet transform on image, We extract moment in low-level subband which was subdivided into subimages and texture feature, contrast of GLCM(Gray Level Co-occurrence Matrix). At first we retrieve the candidate images in database by the ones of image. To perform a more accurate image retrieval, the edge information on the high-level subband was subdivided horizontally, vertically and diagonally. And then, the energy rate of edge per direction was determined and used to compare the energy rate of edge between images for higher accuracy.