• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.51-59
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• 2024

Collecting a dataset with a corresponding labeled gaze vector requires a high cost in the gaze estimation field. In this paper, we suggest a data augmentation of manipulating the gaze of an original image, which improves the accuracy of the gaze estimation model when the number of given gaze labels is restricted. By conducting multi-class gaze bin classification as an auxiliary task and adjusting the latent variable of the diffusion model, the model semantically edits the gaze from the original image. We manipulate a non-binary attribute, pitch and yaw of gaze vector to a desired range and uses the edited image as an augmented train data. The improved gaze accuracy of the gaze estimation network in the semi-supervised learning validates the effectiveness of our data augmentation, especially when the number of gaze labels is 50k or less.

• Korean Journal of Plant Tissue Culture
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• v.22 no.2
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• pp.95-99
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• 1995

We have previously established a system for plant regeneration through somatic embryogenesis and Agrobacterium-mediated transformation of Korean ginseng. In this study to produce a fungus-resistant plant, we introduced a bean chitinase gene into ginseng using the transformation system. A binary vector pChi/748 was constructed by introducing the bean basic chitinase gene into EcoRI site of pGA748 which carries the CaMV 35S promoter governing the introduced gene and neomycin phosphotransferase II(NPT-II)gene as a positive selection marker. Cotyledonary explants were cocultured with A. tumefaciens strain LBA4404 harboring the binary vertor pChi/748 for 48 h, and transferred to MS medium supplemented with l mg/L2,4-D,0.1mg/L kinetin, 100 mg/L kanamycin, and 500mg/L carbenicillin. Kanamycin-resistant calli were formed on the cut surface of cotyledonary explants after one month of culture, and subsequently they gave rise to somatic embryos. Upon transfer onto medium containing 1 mg/L each of BA and GA$_3$, most of them converted to plantlets after 5 weeks of culture. The genomic DNA of eight kanamycin-resistant regenerants was subjected to polymerase chain reaction (PCR) using two specific 21-mer oligonucleotides derived from the chitinase gene. PCR-Southern blot analysis confirmed that the chitinase gene was incorporated into six out of the eight regenerants..

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1889-1891
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• 2004

The differential white blood cell count plays an important role in the diagnosis of different diseases. It is a tedious task to count these classes of cell manually. An automatic counter using computer vision helps to perform this medical test rapidly and accurately. Most commercial-available automatic white blood cell analysis composed mainly 3 steps including segmentation, feature extraction and classification. In this paper we concentrate on the first step in automatic white-blood-cell analysis by proposing a segmentation scheme that utilizes a benefit of active contour. Specifically, the binary image is obtained by thresolding of the input blood smear image. The initial shape of active is then placed roughly inside the white blood cell and allowed to grow to fit the shape of individual white blood cell. The white blood cell is then separated using the extracted contour. The force that drives the active contour is the combination of gradient vector flow force and balloon force. Our purposed technique can handle very promising to separate the remaining red blood cells.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.312-316
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• 2004

The paper presents a technique to identify the boundary of the optic disc in infant retinal digital images using an approach based on active contours (snakes). The technique can be used to be develop a automate system in order to help the ophthalmologist's diagnosis the retinopathy of prematurity (ROP) disease which may occurred on preterm infant,. The optic disc detection is one of the fundamental step which could help to create an automate diagnose system for the doctors we use a new kind of active contour (snake) method has been developed by Chenyang et. al. [1], based on a new type of external force field, called gradient vector flow, or GVF. GVF is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The testing results on a set of infant retinal ROP images verify the effectiveness of the proposed methods. We show that GVF has a large capture range and it's able to move snakes into boundary concavities of optic disc and finally the optic disk boundary was determined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3762-3781
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• 2020

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• ETRI Journal
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• v.40 no.3
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• pp.396-409
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• 2018

Elliptic curve cryptography (ECC) can achieve relatively good security with a smaller key length, making it suitable for Internet of Things (IoT) devices. DNA-based encryption has also been proven to have good security. To develop a more secure and stable cryptography technique, we propose a new hybrid DNA-encoded ECC scheme that provides multilevel security. The DNA sequence is selected, and using a sorting algorithm, a unique set of nucleotide groups is assigned. These are directly converted to binary sequence and then encrypted using the ECC; thus giving double-fold security. Using several examples, this paper shows how this complete method can be realized on IoT devices. To verify the performance, we implement the complete system on the embedded platform of a Raspberry Pi 3 board, and utilize an active sensor data input to calculate the time and energy required for different data vector sizes. Connectivity and resilience analysis prove that DNA-mapped ECC can provide better security compared to ECC alone. The proposed method shows good potential for upcoming IoT technologies that require a smaller but effective security system.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1233-1250
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• 2016

Cracks in plate-like structures are some of the main reasons for destruction of the entire structure. In this study, a novel two-stage methodology is proposed for damage detection of flexural plates using an optimized artificial neural network. In the first stage, location of damages in plates is investigated using curvature-moment and curvature-moment derivative concepts. After detecting the damaged areas, the equations for damage severity detection are solved via Bat Algorithm (BA). In the second stage, in order to efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, multiple damage location assurance criterion index based on the frequency change vector of structures are evaluated using properly trained cascade feed-forward neural network (CFNN) as a surrogate model. In order to achieve the most generalized neural network as a surrogate model, its structure is optimized using binary version of BA. To validate this proposed solution method, two examples are presented. The results indicate that after determining the damage location based on curvature-moment derivative concept, the proposed solution method for damage severity detection leads to significant reduction of computational time compared with direct finite element method. Furthermore, integrating BA with the efficient approximation mechanism of finite element model, maintains the acceptable accuracy of damage severity detection.

• KSBB Journal
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• v.16 no.5
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• pp.480-485
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• 2001

Genetic transformation in dandelion(Taraxacum mongolicum Hand). was studied. We used for transformation by Agrobacterium tumefaciens strian LBA4404 harboring a binary vector pBI121 carrying the CaMV 35S promoter-GUS gene fusion used as a reporter gene and NOS promoter-NPTII gene as a positive selection marker. To obtain transformed plants, leaf explants of dandelion were cocultured with Agrobacterium tumefaciens LBA4404 for 10 mins, then transferred to MS medium containing 1 $\mu$M IAA, 1$\mu$M BA, 100$\mu$g/ML carbenicillin and 50 $\mu$g/ML kanarmycin sulfate. After two weeks of subculture of the explants, Kanamycin-resistant shoots were formed on explants survived. When subjected to GUS histochemical assay, all of the regenerants showed the GUS-positive responses. Plantlets were be be transformed to soil for further growth.

• 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.