• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.862-864
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• 2003

Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum threshold has remained a challenge over decades. Many image segmentation techniques are developed using information about image in other space rather than the image space itself. Most of the technique based on histogram analysis information-theoretic approaches. In this paper, the criterion function for finding optimal threshold is developed using an intensity-based classuncertainty (a histogram-based property of an image) and region-homogeneity (an image morphology-based property). The theory of the optimum thresholding method is based on postulates that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The performance of the proposed method is illustrated on experimental data obtained by W-band millimeter-wave radiometer image under different noise level.

• ETRI Journal
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• v.40 no.6
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• pp.699-713
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• 2018

Orthogonal frequency division multiplexing (OFDM) has been the overwhelmingly prevalent choice for high-data-rate systems due to its superior advantages compared with other modulation techniques. In contrast, a high peak-to-average-power ratio (PAPR) is considered the fundamental obstacle in OFDM systems since it drives the system to suffer from in-band distortion and out-of-band radiation. The partial transmit sequence (PTS) technique is viewed as one of several strategies that have been suggested to diminish the high PAPR trend. The PTS relies upon dividing an input data sequence into a number of subblocks. Hence, three common types of the subblock segmentation methods have been adopted - interleaving (IL-PTS), adjacent (Ad-PTS), and pseudorandom (PR-PTS). In this study, a new type of subblock division scheme is proposed to improve the PAPR reduction capacity with a low computational complexity. The results indicate that the proposed scheme can enhance the PAPR reduction performance better than the IL-PTS and Ad-PTS schemes. Additionally, the computational complexity of the proposed scheme is lower than that of the PR-PTS and Ad-PTS schemes.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2246-2253
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• 2000

In this paper, we show that one can enhance the performance of blind segmentation of phoneme boundaries by adopting the knowledge of Korean syllabic structure and the regions of voiced/unvoiced sounds. eh proposed method consists of three processes : the process to extract candidate phoneme boundaries, the process to detect boundaries of voiced/unvoiced sounds, and the process to select final phoneme boundaries. The candidate phoneme boudaries are extracted by clustering method based on similarity between two adjacent clusters. The employed similarity measure in this a process is the ratio of the probability density of adjacent clusters. To detect he boundaries of voiced/unvoiced sounds, we first compute the power density spectrum of speech signal in 0∼400 Hz frequency band. Then the points where this paper density spectrum variation is greater than the threshold are chosen as the boundaries of voiced/unvoiced sounds. The final phoneme boundaries consist of all the candidate phoneme boundaries in voiced region and limited number of candidate phoneme boundaries in unvoiced region. The experimental result showed about 40% decrease of insertion rate compared to the blind segmentation method we adopted.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.110-112
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• 2002

In this paper, a new technique for Thai isolated speech phoneme segmentation is proposed. Based on Thai speech feature, the isolated speech is first divided into low and high frequency components by using the technique of wavelet decomposition. Then the energy contour of each decomposed signal is computed and employed to locate phoneme boundary. To verity the proposed scheme, some experiments have been performed using 1,000 syllables data recorded from 10 speakers. The accuracy rates are 96.0, 89.9, 92.7 and 98.9% for initial consonant, vowel, final consonant and silence, respectively.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.629-632
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• 2003

This paper has been studied analysis and design of microstrip Rotman lens operating over wide band and wide steering angle by the contour integral method along with the segmentation method. All mutual coupling, internal reflections between ports with exponential taper are taken into account. Equally spaced ports are designed and realized which gives less amplitude ripple at array ports. The measured results of 12 input and 12 output lens show $\pm$1.8 dB insertion loss deviation over 6~18GHz wide frequency range and beam steering accuracy less than 1$^{\circ}$ over $\pm$53$^{\circ}$ angle and agrees well with the analysis results.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.67-74
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• 2016

Drone images with high spatial resolution are emerging as an alternative to previous studies with extraction limits in high density forests. Individual tree in the dense forests were extracted from drone images. To detect the individual tree extracted through the image segmentation process, the image segmentation results were compared between the combination of DSM and all R,G,B band and the combination of DSM and R,G,B band separately. The changes in the tree density of a deciduous forest was experimented by time and image. Especially the image of May when the forests are dense, among the images of March, April, May, the individual tree extraction rate based on the trees surveyed on the site was 50%. The analysis results of the width of crown showed that the RMSE was less than 1.5m, which was the best result. For extraction of the experimental area, the two sizes of medium and small trees were extracted, and the extraction accuracy of the small trees was higher. The forest tree volume and forest biomass could be estimated if the tree height is extracted based on the above data and the DBH(diameter at breast height) is estimated using the relational expression between crown width and DBH.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.363-371
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• 2015

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. Spectral indices can be used to support effective feature extraction by helping to identify abundant surface materials. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral indices. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows from eight band WorldView-2 satellite image using decision tree classification and used the result to draw the appropriate spectral indices for each specific feature extraction. From the results, We identified that spectral band ratios can be applied to distinguish feature classes simply and effectively.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.428-431
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• 2004

A multi-stage hierarchical clustering technique, which is an unsupervised technique, has been proposed in this paper for classifying the hyperspectral data .. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using a context-free similarity measure. This study applied the multistage hierarchical clustering method to the data generated by band reduction, band selection and data compression. The classification results were compared with them using full bands.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.115-127
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• 2009

The purpose of statistical analyses of array-CGH experiment data is to divide the whole genome into regions of equal copy number, to quantify the copy number in each region and finally to evaluate its significance of being different from two. Several statistical procedures have been proposed which include the circular binary segmentation, and a Gaussian based local regression for detecting break points (GLAD) by estimating a piecewise constant function. We propose in this note a penalized spline regression and its simultaneous confidence band(SCB) approach to evaluate the statistical significance of regions of genetic gain/loss. The region of which the simultaneous confidence band stays above 0 or below 0 can be considered as a region of genetic gain or loss. We compare the performance of the SCB procedure with GLAD and hidden Markov model approaches through a simulation study in which the data were generated from AR(1) and AR(2) models to reflect spatial dependence of the array-CGH data in addition to the independence model. We found that the SCB method is more sensitive in detecting the low level copy number alterations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1159-1174
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• 2004

In this paper, we present an efficient algorithm for region analysis of business card images acquired in a PDA by using DCT and information pixel density. The proposed method consists of three parts: region segmentation, information region classification, and text region classification. In the region segmentation, an input business card image is partitioned into 8 f8 blocks and the blocks are classified into information and background blocks using the normalized DCT energy in their low frequency bands. The input image is then segmented into information and background regions by region labeling on the classified blocks. In the information region classification, each information region is classified into picture region or text region by using a ratio of the DCT energy of horizontal and vertical edge components to that in low frequency band and a density of information pixels, that are black pixels in its binarized region. In the text region classification, each text region is classified into large character region or small character region by using the density of information pixels and an averaged horizontal and vertical run-lengths of information pixels. Experimental results show that the proposed method yields good performance of region segmentation, information region classification, and text region classification for test images of several types of business cards acquired by a PDA under various surrounding conditions. In addition, the error rates of the proposed region segmentation are about 2.2-10.1% lower than those of the conventional region segmentation methods. It is also shown that the error rates of the proposed information region classification is about 1.7% lower than that of the conventional information region classification method.