• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.141-148
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• 2011

Most of the previous works on scene change detection algorithm focus on the detection of abrupt rather than gradual changes. In general, gradual scene change detection algorithms require heavy computation. Some of those approaches don't consider the error factors such as flashlights, camera or object movements, and special effects. Many scenes change detection algorithms based on the histogram show better performances than other approaches, but they have computation load problem. In this paper, we proposed a scene change detection algorithm with fast and accurate performance using the vertical and horizontal blocked slice images and their macro block informations. We apply graph cut partitioning algorithm for clustering and partitioning of video sequence using generated spatio-temporal histogram. When making spatio-temporal histogram, we only use the central block on vertical and horizontal direction for performance improvement. To detect camera and object movement as well as various special effects accurately, we utilize the motion vector and type information of the macro block.

• Journal of Life Science
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• v.18 no.4
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• pp.565-571
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• 2008

Due to rapid development of bioinformatics technologies, various biological data have been produced in silico. So now days complicated and large scale biodata are used to accomplish requirement of researcher. Developing visualization and annotation tool using them is still hot issues although those have been studied for a decade. However, diversity and various requirements of users make us hard to develop general purpose tool. In this paper, I propose a novel system, Genome Viewer and Annotation tool (GenoVA), to annotate and visualize among genomes using known information and multiple relation graph. There are several multiple alignment tools but they lose conserved area for complexity of its constrains. The GenoVA extracts all associated information between all pair genomes by extending pairwise alignment. High frequency conserved area and high BLAST score make a block node of relation graph. To represent multiple relation graph, the system connects among associated block nodes. Also the system shows the known information, COG, gene and hierarchical path of block node. In this case, the system can annotates missed area and unknown gene by navigating the special block node's clustering. I experimented ten bacteria genomes for extracting the feature to visualize and annotate among them. GenoVA also supports simple and rough computational annotation of new genome.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.27-39
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• 2002

A locating text technique capable of locating and extracting text blocks in various Web images is presented here. Until now this area of work has been ignored by researchers even if this sort of text may be meaningful for internet users. The algorithms associated with the technique work without prior knowledge of the text orientation, size or font. In the work presented in this research, our text extraction algorithm utilizes useful edge detection followed by histogram analysis on the genuine characteristics of letters defined by text clustering region, to properly perform extraction of the text region that does not depend on font styles and sizes. By a number of experiments we have showed impressively acceptable results.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.359-374
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• 2012

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.256-261
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• 2010

In this paper, a new nonlinear predictor using general regression neural network(GRNN) is proposed for the subband coding of moving pictures. The performance of a proposed nonlinear predictor is compared with BMA(Block Match Algorithm), the most conventional motion estimation technique. As a result, the nonlinear predictor using GRNN can predict well more 2-3dB than BMA. Specially, because of having a clustering process and smoothing noise signals, this predictor well preserves edges in frames after predicting the subband signal. This result is important with respect of human visual system and is excellent performance for the subband coding of moving pictures.

• International Journal of Contents
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• v.14 no.2
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• pp.1-6
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• 2018

In a wireless mobile communication system, a pilot signal has been considered to be a necessary signal for estimating a changing channel between a base station and a terminal. All mobile communication systems developed so far have a specification for transmitting pilot signals. However, although the pilot signal transmission is easy to estimate the channel,(Ed: unclear wording: it is easy to use the pilot signal transmission to estimate the channel?) it should be minimized because it uses radio resources for data transmission. In this paper, we propose a pilotless channel estimation scheme (PCE) by introducing the clustering method of unsupervised learning used in our deep learning into channel estimation.(Ed: highlight- unclear) The PCE estimates the channel using only the data symbols without using the pilot signal at all. Also, to apply PCE to a real system, we evaluated the performance of PCE based on the resource block (RB), which is a resource allocation unit used in LTE. According to the results of this study, the PCE always provides a better mean square error (MSE) performance than the least square estimator using pilots, although it does not use the pilot signal at all. The MSE performance of the PCE is affected by the number of data symbols used and the frequency selectivity of the channel. In this paper, we provide simulation results considering various effects(Ed: unclear, clarify).

• Journal of Korean Society of Forest Science
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• v.83 no.4
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• pp.441-449
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• 1994

The two reflective channels(red and near infrared spectrum) of advanced very high resolution radiometer(AVHRR) data were used to classify primary vegetation cover types in the Korean Peninsula. From the NOAA-11 satellite data archive of 1991, 27 daytime scenes of relatively minimum cloud coverage were obtained. After the initial radiometric calibration, normalized difference vegetation index(NDVI) was calculated for each of the 27 data sets. Four or five daily NDVI data were then overlaid for each of the six months starting from February to November and the maximum value of NDVI was retained for every pixel location to make a monthly composite. The six bands of monthly NDVI composite were nearly cloud free and used for the computer classification of vegetation cover. Based on the temporal signatures of different vegetation cover types, which were generated by an unsupervised block clustering algorithm, every pixel was classified into one of the six cover type categories. The classification result was evaluated by both qualitative interpretation and quantitative comparison with existing forest statistics. Considering frequent data acquisition, low data cost and volume, and large area coverage, it is believed that AVHRR data are effective for vegetation cover type mapping at regional scale.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2294-2304
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• 1999

Our research purpose in this paper is to improve the performance of query processing in GIS(Geographic Information System) by enhancing the I/O performance exploiting parallel I/O and efficient disk access. By packing adjacent spatial data, which are very likely to be referenced concurrently, into one block or continuous disk blocks, the number of disk accesses and the disk access overhead for query processing can be decreased, and this eventually leads to the I/O time decrease. So, in this paper, we proposes EPR(Enhanced Parallel R-tree) indexing method which integrates the parallel I/O method of the previous Parallel R-tree method and a packing-based clustering method. The major characteristics of EPR method are as follows. First, EPR method arranges spatial data in the increasing order of proximity by using Hilbert space filling curve, and builds a packed R-tree by bottom-up manner. Second, with packing-based clustering in which arranged spatial data are clustered into continuous disk blocks, EPR method generates spatial data clusters. Third, EPR method distributes EPR index nodes and spatial data clusters on multiple disks through round-robin striping. Experimental results show that EPR method achieves up to 30% or more gains over PR method in query processing speed. In particular, the larger the size of disk blocks is and the smaller the size of spatial data objects is, the better the performance of query processing by EPR method is.

• Economic and Environmental Geology
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• v.34 no.4
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• pp.355-373
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• 2001

Palaeomagnesim of Paleozoic Tuwibong Type Sequence in Yemi area was studied with a total of 256 core-samples collected from 23 sites. The study area (geographical coordinates: 37.l8$^{\circ}$N, l28.610E) is located between Taebaek and Yongwol belonging to the northeastern part of the Okchon Belt. Thermal cleaning was a most effective method to extract stable characteristic remanent magnetization (ChRM) direction, even though AF cleaning also worked on some specimens. Mean ChRM direction of the Cambrian Hwajol Formation was different from the present-day field direction and showed maximum clustering (max. k value) at 100% bedding-tilt correction. However, it could not pass the fold test. Ordovician Makkol and Kosong Limestones as well as Permian Sadong and Kobangsan Formations have very weak NRM, and were remagnetized into the present-day field direction. ChRM directions from the Carboniferous Hongjom Formation passed both fold and reversal tests. IRM experiments and blocking temperature spectrum indicate that both magnetite and haematite are carrier of the primary magnetization. Palaeomagnetic pole position from the Carboniferous Hongjom Formation is very similar to that of contemporary North China Block (NCB) suggesting that the study area was a part of, or located very near to, the NCB during Carboniferous.