• 대한전자공학회논문지
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• 제25권12호
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• pp.1639-1648
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• 1988

This paper deals with an analyzing method of the Korean syntax to implement a natural language understanding system. A matrix of the syntactic structure is derived by the structural features of the Korean language. The modificatoty and conceptual structures are extracted from the matrix and the predicate logic form is expressed by extracting the phrase, clause and conceptual structure in the analyzing process. This logic form constructs an knowledge base of the sentence and proposes the possibility of the inference.

• The Journal of the Acoustical Society of Korea
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• 제26권3E호
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• pp.104-110
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• 2007

As the exploration of the seabed is extended ever further, automated recognition and classification of sonar images become increasingly important. However, most of the methods ignore the directional information and its effect on the image textures produced. To deal with this problem, we apply 2D Gabor filters to extract the features of sonar images. The filters are designed with constrained parameters to reduce the complexity and to improve the calculation efficiency. Meanwhile, at each orientation, the optimal Gabor filter parameters will be selected with the help of bandwidth parameters based on the Fisher criterion. This method can overcome some disadvantages of the traditional approaches of extracting texture features, and improve the recognition rate effectively.

• 한국산업융합학회 논문집
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• 제5권1호
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• pp.33-38
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• 2002

This paper proposes an efficient method for improving the performance of polynomial adaline using the dimension reduction of independent variables. The adaptive principal component analysis is applied for reducing the dimension by extracting efficiently the features of the given independent variables. It can be solved the problems due to high dimensional input data in the polynomial adaline that the principal component analysis converts input data into set of statistically independent features. The proposed polynomial adaline has been applied to classify the patterns. The simulation results shows that the proposed polynomial adaline has better performances of the classification for test patterns, in comparison with those using the conventional polynomial adaline. Also, it is affected less by the scope of the smoothing factor.

• ETRI Journal
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• 제38권4호
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• pp.776-786
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• 2016

The fairly recent standard of equipping mobile devices with advanced imaging sensors has opened the possibility of conveniently diagnosing skin conditions, anywhere, anytime. For this application, we attempted to estimate skin conditions from a skin image taken by a mobile handheld camera. To estimate the skin conditions, we specifically identified three skin features (pigmentation, pores, and roughness) that can be measured quantitatively from a skin image. The experimental data indicate that the existing thresholding methods are inappropriate for extracting the pigmentation and pore skin features. Thus, we propose a new line-fitting based thresholding method for skin feature detection. We thoroughly evaluated our proposed skin condition estimation method using our skin image database. The experimental results show that our proposed thresholding method can better determine the threshold leading to the most visually plausible detection, when compared to existing methods. We also confirmed that skin conditions can be feasibly estimated using a common mobile handheld camera (for example, a smartphone).

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.476-479
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• 2003

The Rough transform (HT) is often used for extracting global features in binary images, for example curve and line segments, from local features such as single pixels. The HT is useful due to its insensitivity to missing edge points and occlusions, and robustness in noisy images. However, it possesses some disadvantages, such as time and memory consumption due to the number of input data and the selection of an optimal and efficient resolution of the accumulator space can be difficult. Another problem of the HT is in the difficulty of peak detection due to the discrete nature of the image space and the round off in estimation. In order to resolve the problem mentioned above, a possibilistic C-means approach to clustering [1] is used to cluster neighboring peaks. Several experimental results are given.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2002년도 Language, Information, and Computation Proceedings of The 16th Pacific Asia Conference
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• pp.227-236
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• 2002

Japanese coordinate noun phrases by the particle TO are often ambiguous on whether it means two parallel propositions (and), or a mutual case relation (with or against), as deep case structure. It was a hard problem to determine it, though they are widely used. We propose a method of solving the ambiguity by analyzing mutualness of verbs and adjectives. The mutualness is determined by three features of each verb or adjective. The first feature Indicates permission of mutual expression in the subject, and the second In the object. The last shows if a verb is voluntary. Using this method we design a parsing mechanism, where matching of features is represented as neutralization between predicate arguments.

• 한국정밀공학회지
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• 제12권9호
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• pp.86-94
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• 1995

Bead shape in high frequency electric resistance (HER) pipe welding gives useful information on judging current welding conditon. In most welding process, heat input is controlled by skilled operators observing color and shape of bead. In this paper, a visual monitoring system is designed to observe bead shape in HERW pipe welding process by using structured light beam and a C.I.D(Charge injection device) camera. To avoid some difficul- ties arising in extracting stable features of stripe pattern and classifying the extracted features, Kohonen neural network is used to classify such bead shapes. The experimental results show accurate classification performance of the proposed method.

• Journal of Biosystems Engineering
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• 제21권3호
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• pp.315-324
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• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.185-190
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• 2019

For future autonomous cars, it is necessary to recognize various surrounding environments such as lanes, traffic lights, and vehicles. This paper presents a method of speed sign recognition from a single image in automatic driving assistance systems. The detection step with the proposed method emphasizes the color attributes in modified YUV color space because speed sign area is affected by color. The proposed method is further improved by extracting the digits from the highlighted circle region. A sequential cascade AdaBoost classifier is then used in the recognition step for real-time processing. Experimental results show the performance of the proposed algorithm is superior to that of conventional algorithms for various speed signs and real-world conditions.

• 한국정보처리학회논문지
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• 제6권6호
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• pp.1563-1570
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• 1999

본 논문에서는 메모리 기반 추론(MBR : Memory Based Reasoning) 기법에서 사용하는 기억공간과 분류시간의 향상을 위하여 고정 분할 평균(FPA : Fixed Partition Averaging) 알고리즘을 제안하였다. 제안된 방법은 전체 학습패턴들을 대표하는 패턴을 추출하여 효과적인 메모리 사용을 가능하게 하는 방법으로서, 패턴 공간을 일정 개수의 초월평면으로 분할한 후, 초월평면별로 소속된 패턴들의 평균값을 계산하여 대표패턴을 추출한다. 또한 분류성능의 향상을 위하여, 특징과 클래스간의 상호정보(Mutual Information)를 특징의 가중치로 사용하였다.