• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.81-87
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• 2021

Artificial intelligence technology is developing as it enters the fourth industrial revolution. Active researches are going on; visual-based models using CNNs. U-net is one of the visual-based models. It has shown strong performance for semantic segmentation. Although various U-net studies have been conducted, studies on tracking objects with unclear outlines such as gases and smokes are still insufficient. We conducted a U-net study to tackle this limitation. In this paper, we describe how 3D cameras are used to collect data. The data are organized into learning and test sets. This paper also describes how U-net is applied and how the results is validated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.473-476
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• 2007

Location determination of mobile user via RSSI approach has received ample attention from researchers lately. However, it remains a challenging issue due to the complexities of RSSI signal propagation characteristics, which are easily exacerbated by the mobility of user. Hence, a segmentation-based linear spline interpolation method is proposed to cater for the dynamic fluctuation pattern of radio signal in complex environment. This optimization algorithm is proposed in addition to the current radiolocation's (CC2431, Chipcon, Norway) algorithm, which runs on IEEE802.15.4 standard. The enhancement algorithm involves four phases. First phase consists of calibration model in which RSSI values at different static locations are collected and processed to obtain the mean and standard deviation value for the predefined distance. RSSI smoothing algorithm is proposed to minimize the dynamic fluctuation of radio signal received from each reference node when the user is moving. Distances are computed using the segmentation formula obtain in the first phase. In situation where RSSI value falls in more than one segment, the ambiguity of distance is solved by probability approach. The distance probability distribution function(pdf) for each distances are computed and distance with the highest pdf at a particular RSSI is the estimated distance. Finally, with the distances obtained from each reference node, an iterative trilateration algorithm is used for position estimation. Experiment results obtained position the proposed algorithm as a viable alternative for location tracking.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.121-124
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• 1993

This paper is a study on the word recognition using neural network. A limited vocabulary, speaker independent, isolated word recognition system has been built. This system recognizes isolated word without performing segmentation, phoneme identification, or dynamic time wrapping. It needs a static pattern approach to recognize a spatio-temporal pattern. The preprocessing only includes preceding and tailing silence removal, and word length determination. A LPC analysis is performed on each of 24 equally spaced frames. The PARCOR coefficients plus 3 other features from each frame is extracted. In order to simplify a structure of neural network, we composed binary code form to decrease output nodes.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.12-19
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• 1980

A speech recognition system for separately pronounced Korean digits is described. The system is composed of four stages ; parameter extraction, segmentation by voiced-unovied analysis, formant tracking and pattern matching. Digit speech is segmented into an unvoiced segment and/or a voiced one using ZCR and energy measurements, then to estimate the first three formant frequencies a relatively simple formant tracking scheme is applied to the raw formant data extracted from linear prediction spectra. Finally, pattern matching is made using dynamic programmig method. Recognition experiment is carried out for 150 digit utterences spoken by three male speakers, and recgnition rate 94 % is obtained.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.178-182
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• 1988

In this study, the motion compensating interpolation algorithm is presented. The presented algorithm allows the unblutted reconstruction of omitted frames. It is shown that the Walker & Rao's estimation algorithm using modified displaced frame difference combined with rectangulat adaptive measurement window increases the reliability of the estimation results. The remark ably improved image quality is achieved by change detection and segmentation.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1962-1969
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• 1999

Active contours are deformable energy minimizing curves controlled by internal energy and external energy. The internal energy is constraint to preserve a smooth curve, and the external energy guides the curve towards image features. B-spline representation of active contours can be of great benefits in the segmentation and description whose shape is characterized by its defining polygon or control points. Menet et al proposed B-spline representation of active contours based on dynamic programming. The method is simple and efficient by comparing over finite difference method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.1
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• pp.49-58
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• 2012

Buildings become complex and diverse with time. It is difficult to extract individual buildings using only an optical image, because they have similar spectral characteristics to objects such as vegetation and roads. In this study, we propose a method to extract building area and boundary through integrating airborne Light Detection and Ranging(LiDAR) data and aerial images. Firstly, a binary edge map was generated using Edison edge detector after applying Adaptive dynamic range linear stretching radiometric enhancement algorithm to the aerial image. Secondly, building objects on airborne LiDAR data were extracted from normalized Digital Surface Model and aerial image. Then, a temporary building areas were extracted by overlaying the binary edge map and building objects extracted from LiDAR data. Finally, some building boundaries were additionally refined considering positional accuracy between LiDAR data and aerial image. The proposed method was applied to two experimental sites for validation. Through error matrix, F-measure, Jaccard coefficient, Yule coefficient, and Overall accuracy were calculated, and the values had a higher accuracy than 0.85.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.243-245
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• 2016

광학 문자 인식(OCR)을 통해 문서의 글자를 인식할 때 띄어쓰기 오류가 발생한다. 본 논문에서는 이를 해결하기 위해 OCR의 후처리 과정으로 동적 프로그래밍을 이용한 분절(Segmentation) 방식의 띄어쓰기 오류 교정 시스템을 제안한다. 제안하는 시스템의 띄어쓰기 오류 교정 과정은 다음과 같다. 첫째, 띄어쓰기 오류가 있다고 분류된 어절 내의 공백을 모두 제거한다. 둘째, 공백이 제거된 문자열을 동적 프로그래밍을 이용한 분절로 입력 문자열에 대하여 가능한 모든 띄어쓰기 후보들을 찾는다. 셋째, 뉴스 기사 말뭉치와 그 말뭉치에 기반을 둔 띄어쓰기 확률 모델을 참조하여 각 후보의 띄어쓰기 확률을 계산한다. 마지막으로 띄어쓰기 후보들 중 확률이 가장 높은 후보를 교정 결과로 제시한다. 본 논문에서 제안하는 시스템을 이용하여 OCR의 띄어쓰기 오류를 해결할 수 있었다. 향후 띄어쓰기 오류 교정에 필요한 언어 규칙 등을 시스템에 추가한 띄어쓰기 교정시스템을 통하여 OCR의 최종적인 인식률을 향상에 대해 연구할 예정이다.

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.210-219
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• 2010

Consumers differ in both horizontally and vertically. Market segmentation aims to divide horizontally different (or heterogeneous) consumers into more similar (or homogeneous) small segments. A specific consumer, however, may differ in vertically. He (or she) may belong to a different market segment from another one where he (or she) belonged to before. In consumer panel data, the vertical difference can be observed by his (or her) choice among brand alternatives are changing over time. The consumer's vertical difference has been defined as 'dynamics'. In this research, we have developed a binary probit model with random-walk coefficients to capture the consumer's dynamics. With an application to a consumer panel data, we have examined how have the random-walk coefficients changed over time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2355-2373
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• 2011

Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments. These environments include changes in weather as well as challenging traffic conditions, such as shadow effects and jams. To meet real-time requirements, the proposed system first applies a color background to extract moving objects, which are then tracked by considering their relative distances and directions. To achieve robustness and precision, the color background is regularly updated by the proposed algorithm to overcome luminance variations. This paper also proposes a scheme of feedback compensation to resolve background convergence errors, which occur when vehicles temporarily park on the roadside while the background image is being converged. Next, vehicle occlusion is resolved using the proposed prior split approach and through reasoning for rule-based tracking. This approach can automatically detect straight lanes. Following this step, trajectories are applied to derive traffic parameters; finally, to facilitate easy setup, we propose a means to automate the setting of the system parameters. Experimental results show that the system can operate well under various complex traffic conditions in real time.