• Journal of Information Processing Systems
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• v.9 no.1
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.43-49
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• 2004

The classification time required by conventional multi-class SVMs(Support Vector Machines) greatly increases as the number of pattern classes increases. This is due to the fact that the needed set of binary class SVMs gets quite large. In this paper, we propose a method to reduce the number of classes by using nearest neighbor rule (NNR) in the principle component analysis and linear discriminant analysis (PCA+LDA) feature subspace. The proposed method reduces the number of face classes by selecting a few classes closest to the test data projected in the PCA+LDA feature subspace. Results of experiment show that our proposed method has a lower error rate than nearest neighbor classification (NNC) method. Though our error rate is comparable to the conventional multi-class SVMs, the classification process of our method is much faster.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.261-263
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• 2005

In this paper, we implement an identification system using the vein image of the hand. The vein pattern is obtained in the grey-scale 2D image through the infrared-red imaging from back of the hand. Since the frame has lack of clearance, we use some enhancing methods such as the complement, addition, and multiplication to the image to increase the contrast. After Wiener filtering for smoothness of the vein pattern, we transform the image into the binary image with mean function. The binarized image is session thinned and the cross-points in the vein tree are obtained by calculating the number of pixels connected because the image is shaped as a tree. We choose the point and find the nearest to the center if it has majority, where we find the two end points of the selected line. We can get the angle between the two lines joined at the cross-point and store its coordinates, angle, and label the values. The values are used as the feature vector of the vein pattern. This procedure is similar to the human cognition sequences. It is shown that the proposed method is simple for the vein recognition.

• Korean Journal of Plant Tissue Culture
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• v.28 no.6
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• pp.353-357
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• 2001

Transformation of ginseng plants was achieved by biolistic system with cotyledon explants and callus using phosphinothricin acetyl-transferase (PAT) gene resisting to a herbicide of Bialaphos. The binary vector for transformation was constructed with disarmed Ti-plasmid and with double 355 promoter. The introduced NPT II and PAT genes of the transgenic ginseng plants were successfully identified by the PCR, and the survival test on the medium with basta. The transgenic ginseng plants were propagated using repetitive secondary embryogenesis. The transgenic ginseng plantlets had normal structures of roots and shoots, and dormant buds for new year sprouting. We transferred the transgenic plants to greenhouse and observed the continuing growth until a new year.

• Journal of Industrial Technology
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• v.33 no.A
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• pp.31-39
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• 2013

There are two methods to make a distinction of deterioration of high-speed railway track. One is that an administrator checks for each attribute value of track induction data represented in graph and determines whether maintenance is needed or not. The other is that an administrator checks for monthly trend of attribute value of the corresponding section and determines whether maintenance is needed or not. But these methods have a weak point that it takes longer times to make decisions as the amount of track induction data increases. As a field of artificial intelligence, the method that a computer makes a distinction of deterioration of high-speed railway track automatically is based on machine learning. Types of machine learning algorism are classified into four type: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This research uses supervised learning that analogizes a separating function form training data. The method suggested in this research uses SVM classifier which is a main type of supervised learning and shows higher efficiency binary classification problem. and it grasps the difference between two groups of data and makes a distinction of deterioration of high-speed railway track.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.4
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.5
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• pp.375-379
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• 2001

Transgenic plants from hypocotyl segments of buckwheat were produced with the Agrobacterium strain LBA4404 harboring the binary vector pBI121 containing chimeric genes of neomycin phosphotransferase II (npt II) and $\beta$-glucuronidase (gus). Two weeks after co-cultivation with Agrobacterium, most of the hypocotyl segments gradually became brown and died on the selection medium containing 100mg/$\ell$ of kanamycin. Plants regenerated from the hypocotyl explants grown on selection medium were GUS-positive in the leaf, stem and vascular tissues by histochemical assay, and varied in gus activity (440-2568 pmol, 4-MU/mg protein) by fluorimetry. The plants showing GUS activity were confirmed of containing GUS and NPT-II genes by polymerase chain reaction (PCR). Within 3 months, transgenic buckwheat plants were able to obtained from the hypocotyl segments.

• Journal of Plant Biotechnology
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• v.43 no.1
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• pp.66-75
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• 2016

Agrobacterium-mediated gene transfer has recently been developed to improve rice transformation. In this study, 3 different transformation methods were tested including soaking, co-cultivation, and vacuum infiltration. Agrobacterium tumefaciens GV3101 harboring the binary vector pGreen:: LeGSNOR was used in this experiment. This study aimed to identify the most appropriate method for transferring LeGSNOR into rice. Vacuum infiltration of the embryonic calli for 5 min in Ilpum resulted in high transformation efficiency based on confirmation by PCR, RT-PCR, and qRT-PCR analyses. In conclusion, we described the development of an efficient transformation protocol for the stable integration of foreign genes into rice; furthermore, the study results confirmed that PCR is suitable for efficient detection of the integrated gene. The vacuum infiltration system is a potentially useful tool for future studies focusing on transferring important genes into rice seed calli, and may help reduce time and effort.

• Journal of Plant Biotechnology
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• v.36 no.4
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• pp.366-372
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• 2009

This study was conducted to develop new lines expressing the characteristic of early flowering by introducing MdMADS2 gene in chrysanthemum (Dendranthema grandiflorum (Ramat.) Kitamura) ‘Zinba'. Transformation of chrysanthemum was conducted by Agrobacterium tumefaciens LBA4404 harboring the binary vector containing MdMADS2 controlled by double CaMV 35S promoters. Ninety three shoots were regenerated from 1,463 leaf segment explants cultured on the first selection medium (MS basal salts + 1.0 mg/L BA + 0.5 mg/L IAA + 10 mg/L kanamycin + 400 mg/L cefotaxime, pH 5.8) after co-cultivation, and 20 out of the 93 shoots rooted on the second selection medium containing 20 mg/L kanamycin and 400 mg/L cefotaxime. Many escapes (98.6%) were removed on the selection stage for rooting. Nineteen lines were confirmed as transgenic plant with transgene by PCR analysis. Six transgenic plants flowered 2-11 days earlier than non-transgenic plant without big change of phenotype, and especially, 3 (Mo-7, Mo-11, Mo-17) out of 6 transgenic lines showed a significant reduction in days to flowering compared to non-transgenic plant. Introduction and expression of MdMADS2 gene in them were confirmed by Southern and real-time PCR analyses, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.