• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.45-52
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• 2000

In this study, we propose the image segmentation algorithm for facial region segmentation. The proposed algorithm separates the mean image of low frequency band from the differential image of high frequency band in order to make a boundary using HWT, and then we reduce the isolation pixels, projection pixels, and overlapped boundary pixels from the low frequency band. Also the boundaries are detected and simplified by the proposed boundary detection algorithm, which are cleared on the thinning process of 1 pixel unit. After extracting facial image boundary by using the proposed algorithm, we make the mask and segment facial image through matching original image. In the result of facial region segmentation experiment by using the proposed algorithm, the successive facial segmentation have 95.88% segmentation value.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.338-340
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• 1987

The speech recognition system usually consists of two modules, segmentation module and identification module. So, the performance of the system heavily depends on the segmentation accuracy and the segmentation unit. This paper is concerned with the agreeable features for segmentation in syllables. Total energy and two band width energy. (LE:4000-5000Hz and HE:900-3100Hz) are suitable cues for segmentation. And we testify it through the experiment using connected digit.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.67-72
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• 2019

This paper proposes a new method for the recognition of resistor color band using a color segmentation in a HSI color model. The proposed method firstly segments a resistor in a chromatic color as a ROI from a background. Secondly, the color bands of the resistor are segmented by vertical projection profile using both the intensity and the saturation differentiation and finally, it recognizes the colors of the segmented color bands using hue, saturation and intensity values. The final results are the value of the resistor and the names of the recognized color. The proposed method is implemented using C language in Raspberry Pi system with a camera module for a real-time image processing. Experiments were conducted by using various resistor images. The results show that the proposed method is successful for the recognition of resistor color band.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4E
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• pp.134-139
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• 2004

It is generally known that accurate segmentation is very necessary for both an individual word and continuous utterances in speech recognition. It is also commonly known that techniques are now being developed to classify the voiced and the unvoiced, also classifying the plosives and the fricatives. The method for accurate recognition of the phonemes isn't yet scientifically established. Therefore, in this study we analyze the Korean language, using the classification of 'Hunminjeongeum' and contemporary phonetics, with the frequency band, Mel band and Mel Cepstrum, we extract notable features of the phonemes from Korean speech and segment speech by the unit of the phonemes to normalize them. Finally, through the analysis and verification, we intend to set up Phonemic Segmentation System that will make us able to adapt it to both an individual word and continuous utterances.

• The Journal of the Acoustical Society of Korea
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• v.27 no.8
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• pp.418-426
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• 2008

In this paper, we propose an algorithm for the frequency channel segmentation using peaks and valleys in spectrogram. The frequency channel segments means that local groups of channels in frequency domain that could be arisen from the same sound source. The proposed algorithm is based on the smoothed spectrum of the input sound. Peaks and valleys in the smoothed spectrum are used to determine centers and boundaries of segments, respectively. To evaluate a suitableness of the proposed segmentation algorithm before that the grouping stage is applied, we compare the synthesized results using ideal mask with that of proposed algorithm. Simulations are performed with mixed speech signals with narrow band noises, wide band noises and other speech signals.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.592-601
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• 2013

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the appropriate frequency. Secondly, we applied the Gaussian Mixture Model (GMM) to locate the object region. By incorporating the visual attention analysis into object segmentation, our proposed approach is able to narrow the search region for object segmentation, so that the accuracy is increased and the computational complexity is reduced. The experimental results indicate that our proposed approach is efficient for object segmentation in natural images, especially for small objects. Our proposed method significantly outperforms traditional GMM based segmentation.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.660-667
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• 2006

This paper presents a novel region-based snake method for vessel segmentation. According to geometric shape analysis of the vessel structure with different scale, an efficient statistical estimation of vessel branches is introduced into the energy objective function, which applies not only the vessel intensity information, but also geometric information of line-like structure in the image. The defined energy function is minimized using the gradient descent method and a new region-based speed function is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. The narrow band algorithm in the level set framework implements the proposed method, the solution of which is steady. The segmentation experiments are shown on several images. Compared with other geometric active contour models, the proposed method is more efficient and robust.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.705-708
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• 2009

This research describes a neural network-based phoneme segmenter for recognizing spontaneous speech. The input of the phoneme segmenter for spontaneous speech is 16th order mel-scaled FFT, normalized frame energy, ratio of energy among 0~3[KHz] band and more than 3[KHz] band. All the features are differences of two consecutive 10 [msec] frame. The main body of the segmenter is single-hidden layer MLP(Multi-Layer Perceptron) with 72 inputs, 20 hidden nodes, and one output node. The segmentation accuracy is 78% with 7.8% insertion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11A
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• pp.891-897
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• 2009

In the satellite communications, the frequency sharing with other services is important because the service coverage is generally very wide and overlaps with those of other wireless services. This paper introduces a new technique to mitigate interference into the terrestrial stations from the Earth station in the same frequency band by means of band segmentations which are portions of the overall operation frequency band divided by frequency reuse factor. We consider a multi-beam satellite system, where frequency bands are reused in each satellite cell. The terrestrial stations use band segmentations of adjacent satellite cells, and this may decrease the interferences. By this way, the terrestrial and satellite systems can share the same frequency bands efficiently. The simulation is performed at frequency reuse factors, seven and three. The simulation results show that the proposed method can highly reduce the interference level to -168 dBW or -163 dBW depending on the considered frequency reuse factor from -117 dBW at the 90% link availability.

• Journal of KIISE
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• v.43 no.8
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• pp.869-877
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• 2016

In this paper, we propose an automated segmentation method of femoral cartilage in knee MR images using multi-atlas-based locally-weighted voting. The proposed method involves two steps. First, to utilize the shape information to show that the femoral cartilage is attached to a femur, the femur is segmented via volume and object-based locally-weighted voting and narrow-band region growing. Second, the object-based affine transformation of the femur is applied to the registration of femoral cartilage, and the femoral cartilage is segmented via multi-atlas shape-based locally-weighted voting. To evaluate the performance of the proposed method, we compared the segmentation results of majority voting method, intensity-based locally-weighted voting method, and the proposed method with manual segmentation results defined by expert. In our experimental results, the newly proposed method avoids a leakage into the neighboring regions having similar intensity of femoral cartilage, and shows improved segmentation accuracy.