• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.451-458
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and is usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.10
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• pp.90-99
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• 1992

In this paper, we propose a neural network model for speech recognition. The model consists of feature extraction parts and recognition parts. The interconnection model based on ${\Delta}^2$G operator was used for frequency analysis. Two features, global feature and local feature, were extracted from this model. Recognition parts consist of global grouping stage and local grouping stage. When the input pattern was coded by slope method, the recognition rate of speakers, A and B, was 100%. When the test was performed with the data of 9 speakers, the recognition rate of 91.4% was obtained.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.465-468
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• 2022

The incidence of traffic accidents is increasing every year, and Korea is among the top OECD countries. In order to improve this, various road traffic laws are being implemented, and various traffic control methods using equipment such as unmanned speed cameras and traffic control cameras are being applied. However, as drivers avoid crackdowns by detecting the location of traffic control cameras in advance through navigation, a mobile crackdown system that can be cracked down is needed, and research is needed to increase the recognition rate of vehicle license plates on the side of the road for accurate crackdown. This paper proposes a method to improve the vehicle number recognition rate on the road side by applying a gradient correction algorithm using image processing. In addition, custom data learning was conducted using a CNN-based YOLO algorithm to improve character recognition accuracy. It is expected that the algorithm can be used for mobile traffic control cameras without restrictions on the installation location.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2017-2024
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• 1997

The main focus of this paper is the recognition of printed Hangul documents in terms of typeface, character size and character slope for IICS(Intelligent Image Communication System). The fixed-size blocks extracted from documents are analyzed in frequency domain for the typeface classification. The vertical pixel counts and projection profile of bounding box are used for the character size classification and the character slope classification, respectively. The MLP with variable hidden nodes and error back-propagation algorithm is used as typeface classifier, and Mahalanobis distance is used to classify the character size and slope. The experimental results demonstrated the usefulness of proposed system with the mean rate of 95.19% in typeface classification. 97.34% in character size classification, and 89.09% in character slope classification.

• Phonetics and Speech Sciences
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• v.1 no.3
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• pp.133-144
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• 2009

Pharyngeal flap surgery changes the space and shape of the oral cavity and vocal tract, and these changing conditions bring resonance change. The purpose of this study was to determine the most reliable and valuable parameters for evaluating hypernasality to distinguish two patients before and after pharyngeal flap surgery. Each patient was asked to clearly speak the vowels /a/, /i/, /u/, /e/, /o/ for voice recording. There were nine parameters: Formant (F1, F2, F3), Bandwidth (BW1, BW2, BW3), LPC energy slope ($\Delta$ |A2-A1/F2-F1|), and Band Energy (0-500 Hz, 500-1000 Hz) by each vowel. From the results of discrimination analyses on acoustic parameters, the vowels /a/, /e/ appeared to be insignificant but vowels /i/, /u/, /o/ appeared to be efficient in the separation. A 95%, 100%, and 100% recognition score could be reached when vowels /i/, /u/, and /o/ were analyzed. The results showed that F2, BW3, and LPC slope are more important parameters than the others. Finally, there is a relation between perceptual evaluation score and LPC energy slope of acoustic parameters by least square slope.

• Korean Journal of Remote Sensing
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• v.5 no.2
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• pp.123-131
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• 1989

The inventory maps of landslide deposits show where landsliding has occured in the past, and serve as a general guide to slope stability. Isopleth maps derived from those inventory maps, generalized and quantify the areal distribution of landslide deposits in contour form. Isopleth maps can provide an economical means for the recognition of landslide activity and assessing the degree of landslide hazard in a large area, especially rural areas. Isopleth maps of Bonggye area, where the degree of landslide hamedial efforts during the period of 1954-1971.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2020-2023
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• 2003

In this paper, our proposed system uses the regional Gabor wavelet and Neural Network to implement face recognition similar to human face recognition system, because the Gator wavelet expresses visual recognition system of human mathematically and the regional Neural Network is robust to white noise and partial illumination. This system consists of two stages of building database and recognizing face. One is composed by using the supervised learning of Neural Network. At this time, the Neural Network is applied to the upper and the lower part of face images respectively. The Backpropagation algorithm is used to learn Neural Network. Another consists of calibration of slope of face image, measurement of illumination variant using deviation with average face image and similarity comparison using Euclidean distance measure.

• Journal of the Korean Institute of Rural Architecture
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• v.7 no.3
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• pp.27-35
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• 2005

This study is about finding out characteristic of recognition individual housing in seaside of Donghae. To accomplish this purpose, we survey the 150 houses related to the landscape. Thus the major analysis is to take basic data, such as image(modern, western, traditional, etc) about exterior form of housing corresponding to the landscape. The result summarized as follows First, the elements for the characteristic of recognition were exterior material finish, exterior color, roof type, roof material finish, window size, roof slope, area of wall vs roof. Second, the image of traditional housing was very insufficient to plan landscape of housing with design elements. This research suggests that landscape housing of future is to be environmental landscape design and the proper design is to be various considering not only user's preference but also control of landscape.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.935-943
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• 1993

In this paper, robust recognition system for tilted facial image was developed. At first, standard facial image and lilted facial image are captured by CCTV camera and then transformed into binary image. The binary image is processed in order to obtain contour image by Laplacian edge operator. We trace and delete outermost edge line and use inner contour lines. We label four inner contour lines in order among the inner lines, and then we extract left and right eye with known distance relationship and with two eyes coordinates, and calculate slope information. At last, we rotate the tilted image in accordance with slope information and then calculate the ten distance features between element and element. In order to make the system invariant to image scale, we normalize these features with distance between left and righ eye. Experimental results show 88% recognition rate for twenty five face images when tilted degree is considered and 60% recognition rate when tilted degree is not considered.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.39-45
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• 1995

This paper presents noisy speech recognition method based on spectral mapping techniques of speaker adaptation method. In the presented method, the spectral mapping training makes the spectral distortion of noisy speech reduced, and for the more correctively spectral mapping, let the adjustment window;s slope be adaptive to several word lengths. As a result of recognition experiment, the recognition rate is higher than that of the conventional method using VQ and DTW without noise processing. Even when SNR level is 0 dB, the recognition rate is 10 times more than that using the conventional method. It is confirmed that the speacker adaptation technique using the spectral mapping training has an ability to improve the recognition performance for noisy speech.