• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.48-57
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• 2010

Previous post sender recognition study was focused on recognizing the address of receiver. Relatively, there was lack of study to recognize the information of sender's address. Post sender recognition study is necessary for the service and application using sender information such as returning. This paper did the experiment and suggested how to recognize post sender using SIFT. Although SIFT shows great recognition rate, SIFT had problems with time and mis-recognition. One is increased time to match keypoints in proportion as the number of registered model. The other is mis-recognition of many similar keypoints even though they are all different models due to the nature of post sender. To solve the problem, this paper suggested SIFT adding distance function and did the experiment to compare time and function. In addition, it is suggested how to register and classify models automatically without the manual process of registering models.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.58-67
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• 1994

Hidden Markov Model (HMM)-based algorithms have been used successfully in many speech recognition systems, especially large vocabulary systems. Although general purpose processors can be employed for the system, they inevitably suffer from the computational complexity and enormous data. Therefore, it is essential for real-time speech recognition to develop specialized hardware to accelerate the recognition steps. This paper concerns with a real-time implementation of an isolated word recognition system based on HMM. The speech recognition system consists of a host computer (PC), a DSP board, and a prototype Viterbi scoring board. The DSP board extracts feature vectors of speech signal. The Viterbi scoring board has been implemented using three field-programmable gate array chips. It employs a hardware-efficient Viterbi scoring architecture and performs the Viterbi algorithm for HMM-based speech recognition. At the clock rate of 10 MHz, the system can update about 100,000 states within a single frame of 10ms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.5
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• pp.266-279
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• 2003

In this paper, we propose machine printed character recognition methods which utilize the character type information and divide the character clusters. The characters are subdivided into a total of seven types, of which six types are for Hangul according to the grapheme combination fashions and one type for English characters, numerals, and symbols. According to the character type, we separate input character image into several recognition units and recognize them by using the direction angle feature. The recognition for each character type is completed by combining recognition units which are recognized by neural networks respectively For combining a total of seven character recognizers, we implemented seven methods such as switching method, integrating method, and their several variants. As experimental results, we obtained 98.2% recognition rate of simple switching method, 90.54% of integrating one, and between 97.35% and 98.65% of five variants.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.152-159
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• 2010

Existing character recognition algorithm recognize characters in simple conditional. It has the disadvantage that recognition rates often drop drastically when input document image has low quality, rotated text, various font or size text because of external noise or data loss. In this paper, proposes the optical character recognition algorithm which using bicubic interpolation method for the catalog retrieval when the input image has rotated text, blurred, various font and size. In this paper, applied optical character recognition algorithm consist of detection and recognition part. Detection part applied roberts and hausdorff distance algorithm for correct detection the catalog of book. Recognition part applied bicubic interpolation to interpolate data loss due to low quality, various font and size text. By the next time, applied rotation for the bicubic interpolation result image to slant proofreading. Experimental results show that proposal method can effectively improve recognition rate 6% and search-time 1.077s process result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.924-942
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• 2020

The facial expression is diverse and various among persons due to the impact of the psychology factor. Whilst the facial action is comparatively steady because of the fixedness of the anatomic structure. Therefore, to improve performance of the action unit recognition will facilitate the facial expression recognition and provide profound basis for the mental state analysis, etc. However, it still a challenge job and recognition accuracy rate is limited, because the muscle movements around the face are tiny and the facial actions are not obvious accordingly. Taking account of the moving of muscles impact each other when person express their emotion, we propose to make full use of co-occurrence relationship among action units (AUs) in this paper. Considering the dynamic characteristic of AUs as well, we adopt the 3D Convolutional Neural Network(3DCNN) as base framework and proposed to recognize multiple action units around brows, nose and mouth specially contributing in the emotion expression with putting their co-occurrence relationships as constrain. The experiments have been conducted on a typical public dataset CASME and its variant CASME2 dataset. The experiment results show that our proposed AU co-occurrence constraint 3DCNN based AU recognition approach outperforms current approaches and demonstrate the effectiveness of taking use of AUs relationship in AU recognition.

• The Korean Journal of Food And Nutrition
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• v.26 no.2
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• pp.216-225
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• 2013

This study investigated the interrelations among acculturative stress, recognition, preference and eating frequency of Korean traditional food by Chinese students in Korea. The acculturative stress score was $3.06{\pm}0.32$, 'homesickness' score was the highest ($3.92{\pm}0.62$) and 'guiltscore' score was the lowest ($2.28{\pm}1.04$). The rate of preferred for Korean food was low (20.5%). The places for eating Korean food were 'restaurant' (49.6%) and 'school cafeteria' (41.1%). The cooking experience regarding Korean food was 45.0% and they had cooked 'Bulgogi' (26.7%) and 'Bibimbap' (21.9%). The recognition score for Korean traditional food was $0.70{\pm}0.27$. The preference score for Korean traditional food was $3.14{\pm}0.54$, and the favorite foods were 'Galbi' and 'Galbitang' while 'Ggakdugi' was the lowest. The eating frequency for Korean traditional food was $2.15{\pm}0.82$, and 'Baechukimchi' and 'Bibimbap' were comparatively high. The acculturative stress showed no correlation with the recognition, preference and eating frequency of Korean traditional food. The recognition of Korean traditional food correlated positively with the eating frequency (r=0.175, p<0.05). The preference of Korean traditional food had a significant effect on eating frequency (r=0.274, p<0.001), and the highest positive correlation was shown in 'Ddeokbokki' (r=0.470). The explanation power ($R^2$) of recognition and preference on eating frequency was 0.098. This study showed the interrelations among recognition, preference, and eating frequency of Korean traditional food except for acculturative stress.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.31-39
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• 2011

The facial recognition algorithms using Gabor wavelet based face graph produce very good performance while they have some weakness such as a large amount of computation and an irregular result depend on initial location. We proposed a fully automatic facial recognition algorithm using a Gabor feature based geometric deformable face graph matching. The initial location and size of a face graph can be selected using Adaboost detection results for speed-up. To find the best face graph with the face model graph by updating the size and location of the graph, the geometric transformable parameters are defined. The best parameters for an optimal face graph are derived using an optimization technique. The simulation results show that the proposed algorithm can produce very good performance with recognition rate 96.7% and recognition speed 0.26 sec for FERET database.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.654-662
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• 2010

In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using second-stage cascade method, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: (i) Histogram of Oriented Gradient (HOG) which includes gradient information and differential magnitude; (ii) Curvature-HOG which is based on four different curvature features per pixel. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using both HOG and curvature-HOG. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. For the recognition-failed image, the other feature and strong classification will be used for the second stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method.

• Journal of the Korean Society of Food Culture
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• v.29 no.6
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• pp.528-538
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• 2014

The purpose of this study was to investigate the relationships among recognition, preference, and purchasing characteristics for Changnyeong onions and garlic as well as festival satisfaction among participants at agricultural product festivals in the Changnyeong region. Results showed that festival satisfaction of 'place of festival' was highest while 'convenience facility & event contents' earned the lowest scores. Most subjects (90.5%) had purchased Changnyeong agricultural and processed products. A major purchasing type was fresh agricultural products (66.7%). The pathways to recognize Changnyeong agricultural products were mostly 'promotion by related institutions' (22.0%), 'family relatives' (20.8%), 'mass media' (16.6%), and 'festivals and events' (16.1%). The most considered factors for purchasing regional products were 'geographical origin' and 'ingredients'. Changnyeong onion showed higher scores for recognition and preference and rate of purchase experience and intention than for garlic. The correlation coefficients of recognition and preference for onion and galic were 0.603 (p<0.001) and 0.598 (p<0.001), respectively. The explanation power ($R^2$) of related variables for purchase of Changnyeong onions was 0.258. The regression coefficients (${\beta}$) for 'recognition', 'preference' and 'convenient facility & event contents' were positive, whereas the regression coefficient for 'price' was negative. Recognition, preference, and convenient facility & event contents with garlic purchase showed a positive relationship ($R^2$=0.253). The most effective promotion method to increase sales of Changnyeong agricultural products was 'local festivals and events' (27.8%).

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.70-78
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• 2007

The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.