• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.97-105
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• 2017

This study is the realization study recognizing the Korean gothic printing letter. This study defined the new grapheme by using the connection ingredient and had the graphemes recognized by means of the feature dots of the isolated dot, end dot, 2-line gathering dots, more than 3 lines gathering dots, and classified the characters by means of the arrangement information of the graphemes and the layers that the graphemes form within the characters, and made the character database for the recognition by using them. The layers and the arrangement information of the graphemes consisting in the characters were presumed by using the weight center position information of the graphemes extracted from the characters to recognize and the information of the graphemes obtained by vertically exploring from the weight center of each grapheme, and it recognized the characters by judging and comparing the character groups of the database by means of the information which was secured this way. 350 characters were used for the character recognition test and about 97% recognition result was obtained by recognizing 338 characters.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.3
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• pp.269-281
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• 2001

This thesis deals with a novel font-dependent Hangul recognition algorithm invariant to position translation, scaling, and rotation using circular pattern vectors. The proposed algorithm removes noise from input letters using binary morphology and generates the circular pattern vectors. The generated circular pattern vectors represent spatial distributions on several concentric circles from the center of gravity in a given letter. Then the algorithm selects the letter minimizing the distance between the reference vectors and the generated circular pattern vectors. In order to estimate performances of the proposed algorithm, the completed Batang Hangul 2,350 letters were used as test images with scaling and rotational transformations. Experimental results show that the proposed algorithm are better than conventional algorithm using the ring projection in the recognition rates of Hangul letters with scaling and rotational transformation.

• ETRI Journal
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• v.28 no.6
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• pp.811-814
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• 2006

In this letter, we introduce confusion-based confidence measures for detecting an impostor in speaker recognition, which does not require an alternative hypothesis. Most traditional speaker verification methods are based on a hypothesis test, and their performance depends on the robustness of an alternative hypothesis. Compared with the conventional Gaussian mixture model-universal background model (GMM-UBM) scheme, our confusion-based measures show better performance in noise-corrupted speech. The additional computational requirements for our methods are negligible when used to detect or reject impostors.

• ETRI Journal
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• v.34 no.3
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• pp.466-469
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• 2012

In this letter, we propose a dual autostereoscopic display platform employing a natural interaction method, which will be useful for sharing visual data with users. To provide 3D visualization of a model to users who collaborate with each other, a beamsplitter is used with a pair of autostereoscopic displays, providing a visual illusion of a floating 3D image. To interact with the virtual object, we track the user's hands with a depth camera. The gesture recognition technique we use operates without any initialization process, such as specific poses or gestures, and supports several commands to control virtual objects by gesture recognition. Experiment results show that our system performs well in visualizing 3D models in real-time and handling them under unconstrained conditions, such as complicated backgrounds or a user wearing short sleeves.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1253-1262
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• 2013

This study observes not only how much those who studied Statistics during the college years feel that Statistics is difficult but also why they felt it was difficult. Most of the targeted researchers, 80.8 percent, say "Statistics was difficult". They selected the item "textbooks were hard to understand" as the main reason (62.5%). Based on the explanatory survey of text books, many textbooks do not distinguish the small letter, x from the capital letter, X. Hence, in this study, one of the main reasons why most of the researchers felt Statistics was difficult must be the ambiguousness of the notations. If authors keep in mind the importance of the difference between capital letters and small letters in Statistics, the Statistics learners' recognition of difficulty of Statistics will decline.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.777-784
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• 2003

In this Paper. we propose a recognition method of machine printed characters based on the combination of recognition units using multiple neural networks. In our recognition method, the input character is classified into one of 7 character types among which the first 6 types are for Hangul character and the last type is for non-Hangul characters. Hangul characters are recognized by several MLP (multilayer perceptron) neural networks through two stages. In the first stage, we divide Hangul character image into two or three recognition units (HRU : Hangul recognition unit) according to the combination fashion of graphemes. Each recognition unit composed of one or two graphemes is recognized by an MLP neural network with an input feature vector of pixel direction angles. In the second stage, the recognition aspect features of the HRU MLP recognizers in the first stage are extracted and forwarded to a subsequent MLP by which final recognition result is obtained. For the recognition of non-Hangul characters, a single MLP is employed. The recognition experiments had been performed on the character image database collected from 50,000 real letter envelope images. The experimental results have demonstrated the superiority of the proposed method.

• Korean Journal of Biological Psychiatry
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• v.11 no.2
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• pp.155-164
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• 2004

Objective:The present study examined the association between basic neurocognitive functions and emotional recognition in chronic schizophrenia. Furthermore, to Investigate cognitive variable related to emotion recognition in Schizophrenia. Methods:Forty eight patients from the Yongin Psychiatric Rehabilitation Center were evaluated for neurocognitive function, and Emotional Recognition Test which has four subscales finding emotional clue, discriminating emotions, understanding emotional context and emotional capacity. Measures of neurocognitive functioning were selected based on hypothesized relationships to perception of emotion. These measures included:1) Letter Number Sequencing Test, a measure of working memory;2) Word Fluency and Block Design, a measure of executive function;3) Hopkins Verbal Learning Test-Korean version, a measure of verbal memory;4) Digit Span, a measure of immediate memory;5) Span of Apprehension Task, a measure of early visual processing, visual scanning;6) Continuous Performance Test, a measure of sustained attention functioning. Correlation analyses between specific neurocognitive measures and emotional recognition test were made. To examine the degree to which neurocognitive performance predicting emotional recognition, hierarchical regression analyses were also made. Results:Working memory, and verbal memory were closely related with emotional discrimination. Working memory, Span of Apprehension and Digit Span were closely related with contextual recognition. Among cognitive measures, Span of Apprehension, Working memory, Digit Span were most important variables in predicting emotional capacity. Conclusion:These results are relevant considering that emotional information processing depends, in part, on the abilities to scan the context and to use immediate working memory. These results indicated that mul- tifaceted cognitive training program added with Emotional Recognition Task(Cognitive Behavioral Rehabilitation Therapy added with Emotional Management Program) are promising.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1496-1509
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• 2005

In this paper, an adaptive license plate recognition system which detects and recognizes license plate at real-time by using projected plane convolution and Decision Tree Classifier is proposed. And it was tested in circumstances which presence of complex background. Generally, in expressway tollgate or gateway of parking lots, it is very difficult to detect and segment license plate because of size, entry angle and noisy problem of vehicles due to CCD camera and road environment. In the proposed algorithm, we suggested to extract license plate candidate region after going through image acquisition process with inputted real-time image, and then to compensate license size as well as gradient of vehicle with change of vehicle entry position. The proposed algorithm can exactly detect license plate using accumulated edge, projected convolution and chain code labeling method. And it also segments letter of license plate using adaptive binary method. And then, it recognizes license plate letter by applying hybrid pattern vector method. Experimental results show that the proposed algorithm can recognize the front and rear direction license plate at real-time in the presence of complex background environments. Accordingly license plate detection rate displayed $98.8\%$ and $96.5\%$ successive rate respectively. And also, from the segmented letters, it shows $97.3\%$ and $96\%$ successive recognition rate respectively.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.81-88
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• 2007

We implement automobile license plates recognition system. These days automobile license plate recognition systems are widely used for tracing stolen cars. managing parking facilities, ticketing speeding cars, and so on. Recognition systems largely consist of three parts plates extraction, segments extraction, and segment recognition. For plates extraction, we measure the degree of inclination of plate. We use filters that extract only the horizontal components of the front of an automobile to measure the degree of inclination. For segment extraction, we trace the change of the number of blocks that consist solely of foreground pixels or background pixels as the horizontal scanning line moves along upward. For recognition of each individual letter or digit, we devise a variant of template matching method, called comparative template matching. Through experiments, we show that comparative template matching is less prone misled by noises and exhibits higher performance compared to the traditional method of template matching or histogram based recognition.

• ETRI Journal
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• v.29 no.2
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• pp.243-245
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• 2007

This letter presents a new feature extraction method based on the private energy map (PEM) technique to utilize the energy characteristics of a facial image. Compared with a non-facial image, a facial image shows large energy congestion in special regions of discrete cosine transform (DCT) coefficients. The PEM is generated by energy probability of the DCT coefficients of facial images. In experiments, higher face recognition performance figures of 100% for the ORL database and 98.8% for the ETRI database have been achieved.