• 제목/요약/키워드: 레인 및 밴드 추출

검색결과 7건 처리시간 0.021초

(Image Analysis of Electrophoresis Gels by using Region Growing with Multiple Peaks) (다중 피크의 영역 성장 기법에 의한 전기영동 젤의 영상 분석)

  • 김영원;전병환
    • Journal of KIISE:Software and Applications
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    • 제30권5_6호
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    • pp.444-453
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    • 2003
  • Recently, a great interest of bio-technology(BT) is concentrated and the image analysis technique for electrophoresis gels is highly requested to analyze genetic information or to look for some new bio-activation materials. For this purpose, the location and quantity of each band in a lane should be measured. In most of existing techniques, the approach of peak searching in a profile of a lane is used. But this peak is improper as the representative of a band, because its location does not correspond to that of the brightest pixel or the center of gravity. Also, it is improper to measure band quantity in most of these approaches because various enhancement processes are commonly applied to original images to extract peaks easily. In this paper, we adopt an approach to measure accumulated brightness as a band quantity in each band region, which Is extracted by not using any process of changing relative brightness, and the gravity center of the region is calculated as a band location. Actually, we first extract lanes with an entropy-based threshold calculated on a gel-image histogram. And then, three other methods are proposed and applied to extract bands. In the MER method, peaks and valleys are searched on a vertical search line by which each lane is bisected. And the minimum enclosing rectangle of each band is set between successive two valleys. On the other hand, in the RG-1 method, each band is extracted by using region growing with a peak as a seed, separating overlapped neighbor bands. In the RG-2 method, peaks and valleys are searched on two vertical lines by which each lane is trisected, and the left and right peaks nay be paired up if they seem to belong to the same band, and then each band region is grown up with a peak or both peaks if exist. To compare above three methods, we have measured the location and amount of bands. As a result, the average errors in band location of MER, RG-1, and RG-2 were 6%, 3%, and 1%, respectively, when the lane length is normalized to a unit value. And the average errors in band amount were 8%, 5%, and 2%, respectively, when the sum of band amount is normalized to a unit value. In conclusion, RG-2 was shown to be more reliable in the accuracy of measuring the location and amount of bands.

DNA Band Recognition using the Topographical Features of Images (영상의 지형적 특징에 의한 유전밴드 인식)

  • Hwang, Deok-In;Gong, Seong-Gon;Jo, Seong-Won;Jo, Dong-Seop;Lee, Seung-Hwan
    • Journal of KIISE:Software and Applications
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    • 제26권11호
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    • pp.1350-1358
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    • 1999
  • 이 논문에서는 유전밴드 영상신호에 포함되어 있는 지형적 특징을 이용하여 밝기의 변화가 일정하지 않은 유전밴드를 인식하는 방법을 연구하였다. 유전밴드는 동일인을 식별하는데 있어서 지문보다 높은 신뢰성을 가지고 있으므로, 유전밴드 영상에서 유전밴드의 유무와 위치를 자동적으로 검출하는 것은 매우 중요하다. 레인내의 밝기의 변화가 일정한 유전밴드는 미분연산자에 의해 검출할 수 있지만, 밝기의 변화가 일정하지 않은 레인내의 유전밴드는 일반적인 인식방법에 의해서는 검출하기 어렵다. 따라서 유전밴드 영상으로부터 지형적 특징을 추출하고, 이것으로부터 계산한 곡률(curvature)의 크기에 의해 유전밴드를 인식함으로써 레인의 밝기가 변화하는 경우에도 효과적으로 인식하였다.Abstract This paper presents recognition of DNA band using the topographical features of DNA band images. The DNA band provides a more reliable way of identification than fingerprints. Recognition based on differentiation operators can easily detect the DNA band if the brightness of lane in the image is almost uniform. When the brightness of the lane changes gradually, the DNA bands are hard to be recognized. Using the curvature magnitude of the lane computed from topographic features extracted from DNA images, the DNA bands are efficiently recognized in the lane whose brightness changes.

Searching Spectrum Band of Crop Area Based on Deep Learning Using Hyper-spectral Image (초분광 영상을 이용한 딥러닝 기반의 작물 영역 스펙트럼 밴드 탐색)

  • Gwanghyeong Lee;Hyunjung Myung;Deepak Ghimire;Donghoon Kim;Sewoon Cho;Sunghwan Jeong;Bvouneiun Kim
    • Smart Media Journal
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    • 제13권8호
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    • pp.39-48
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    • 2024
  • Recently, various studies have emerged that utilize hyperspectral imaging for crop growth analysis and early disease diagnosis. However, the challenge of using numerous spectral bands or finding the optimal bands for crop area remains a difficult problem. In this paper, we propose a method of searching the optimized spectral band of crop area based on deep learning using the hyper-spectral image. The proposed method extracts RGB images within hyperspectral images to segment background and foreground area through a Vision Transformer-based Seformer. The segmented results project onto each band of gray-scale converted hyperspectral images. It determines the optimized spectral band of the crop area through the pixel comparison of the projected foreground and background area. The proposed method achieved foreground and background segmentation performance with an average accuracy of 98.47% and a mIoU of 96.48%. In addition, it was confirmed that the proposed method converges to the NIR regions closely related to the crop area compared to the mRMR method.

Effective Removal of Undesired signals in Measurements of Radar Target Characteristics (레이다 표적의 특성 측정시 원하지 않는 신호의 효율적인 제거)

  • 김수범;김영수
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • 제10권6호
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    • pp.889-899
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    • 1999
  • A technique to obtain an exact frequency characteristics of desired targets in radar measurements is presented. The pulsing network composed of two RF switches was installed between the Network Analyzer and the antenna, and the backscattering from a metal sphere was measured at X-band. It is shown that the pulsing effectively eliminated undesired returns from antenna and other circuitry of the systems. The antenna return was suppressed by more than 60 dB, and the signal-to-noise ratio was improved drastically. The pulsed frequency data were processed to extract the responses of the desired target. The result agrees well with the theoretical backscattering characteristics of the sphere. The methods presented here are applicable to RCS measurements in compact ranges, and also to the backscattering measurements of distributed targets outdoors.

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A Study on the Application of IHS Transformation Technique for the Enhancement of Remotely Sensed Data Classification (리모트센싱 데이터의 분류향상을 위한 IHS 변환기법 적용)

  • Yeon, Sangho
    • Journal of the Korean Association of Geographic Information Studies
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    • 제1권1호
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    • pp.109-117
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    • 1998
  • To obtain new information using a single remotely sensed image data is limited to extract various information. Recent trends in the remote sensing show that many researchers integrate and analyze many different forms of remotely sensed data, such as optical and radar satellite images, aerial photograph, airborne multispectral scanner data and land spectral scanners. Korean researchers have not been using such a combined dataset yet. This study intended to apply the technique of integration between optical data and radar data(SAR) and to examine the output that had been obtained through the technique of supervised classification using the result of integration. As a result, we found of better enhanced image classification results by using IHS conversion than by using RGB mixed and interband correlation.

Experimental Study on DEM Extraction Using InSAR and 3-Pass DInSAR Processing Techniques (InSAR 및 3-Pass DInSAR 처리기법을 적용한 DEM 추출에 대한 실험 연구)

  • Bae, Sang-Woo;Lee, Jin-Duk
    • The Journal of the Korea Contents Association
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    • 제7권3호
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    • pp.176-186
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    • 2007
  • As SAR data have the strong point that is not influenced by weather or light amount in comparison with optical sensor data, they are highly useful for temporary analysis and can be collected in time of unforeseen circumstances like disaster. This study is to extract DEM from L-band data of JERS-1 SAR imagery using InSAR and DInSAR processing techniques. As a result of analyzing the extracted coherence and interferogram images, it was shown that the DInSAR 3-pass method produces more suitable coherence values than the InSAR method. The accuracies of DEM extracted from the SAR data were evaluated by employing the DEM derived from the digital topographic maps of 1:5000 scale as reference data. And it was ascertained that baselines between antenna locations largely affect the accuracy of extracted DEM.

Three Dimensional Analysis Using Digital Elevation Model on the Coastal Landform of the Sacheon Bay, South Sea of Korea (수치고도 모델을 이용한 사천만 해안지역의 3차원 지형분석)

  • Lee, Min-Boo;Kim, Nam-Shin;Han, Kyun-Hyeung
    • Journal of the Korean association of regional geographers
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    • 제9권2호
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    • pp.203-216
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    • 2003
  • The process of constructing coastal digital elevation model(DEM), for the 3 dimensional analysis, is composed by abstracting land layers for land elevation and water depth, reprojecting UTM, relocating geographical grid, and interpolating works. The geomorphic set of shallow sea, including tidal current, tidal zone deposition, and water depth distribution, was analyzed by eye search of Landsat TM image, masking of land zone, band combination and regression analysis. Some horizontal differences, between combined DEM and surveyed data of shallow sea, was corrected for analysis. Analyzed geomorphic elements are stream channel, alluvial fan, coastal terrace, tidal current. and shallow sea bank. Results of analysis present that transported fluvial materials influence tidal sedimentation, especially from Gahwacheon river, for the role of artificial draining flooding waters from Jinyang Reservoir, almost in the summer season. In the coastal area with less tidal current, more fine materials are deposited. The influence of currental deposition are higher on small pockets with west coast of well developed terraces. The lower skirt of alluvial fans developed into the tidal zone of shallow sea. Small pocket type bays are closed by coastal current, and less influenced from tidal deposition. The bank of Jinju Bay are developed originally from submerging of remnant erosional mountain ranges, and play on the role of trapping fine materials.

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