• Title/Summary/Keyword: Multiple-Color Model

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STANDARIZING THE EXTRATERRESTRIAL SOLAR IRRADIANCE SPECTRUM FOR CAL/VAL OF GEOSTATIONARY OCEAN COLOR IMAGER (GOCI)

  • Shanmugam, Palanisamy;Ahn, Yu-Hwan
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.86-89
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    • 2006
  • Ocean color remote sensing community currently uses the different solar irradiance spectra covering the visible and near-infrared in the calibration/validation and deriving products of ocean color instruments. These spectra derived from single and / or multiple measurements sets or models have significant discrepancies, primarily due to variation of the solar activity and uncertainties in the measurements from various instruments and their different calibration standards. Thus, it is prudent to examine model-to-model differences and select a standard reference spectrum that can be adopted in the future calibration and validation processes, particularly of the first Geostationary Ocean Color Imager (GOCI) onboard its Communication Ocean and Meterological Satellite (COMS) planned to be launched in 2008. From an exhaustive survey that reveals a variety of solar spectra in the literature, only eight spectra are considered here seeing as reference in many remote sensing applications. Several criteria are designed to define the reference spectrum: i.e., minimum spectral range of 350-1200nm, based completely or mostly on direct measurements, possible update of data and less errors. A careful analysis of these spectra reveals that the Thuillier 2004 spectrum seems to be very identical compared to other spectra, primarily because it represents very high spectral resolution and the current state of the art in solar irradiance spectra of exceptionally low uncertainty ${\sim}0.1%.$ This study also suggests use of the Gueymard 2004 spectrum as an alternative for applications of multispectral/multipurpose satellite sensors covering the terrestrial regions of interest, where it provides spectral converge beyond 2400nm of the Thuillier 2004 spectrum. Since the solar-activity induced spectral variation is about less than 0.1% and a large portion of this variability occurs particularly in the ultraviolet portion of the electromagnetic spectrum that is the region of less interest for the ocean color community, we disregard considering this variability in the analysis of solar irradiance spectra, although determine the solar constant 1366.1 $Wm^{-2}$ to be proposed for an improved approximation of the extraterrestrial solar spectrum in the visible and NIR region.

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Application of Near Infrared Spectroscopy for Nondestructive Evaluation of Color Degree of Apple Fruit (사과 착색도의 비파괴측정을 위한 근적외분광분석법의 응용)

  • Sohn, Mi-Ryeong;Cho, Rae-Kwang
    • Food Science and Preservation
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    • v.7 no.2
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    • pp.155-159
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    • 2000
  • Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).

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A Differences in Preference and Evaluation on the Image of Make-up (Part II) -Focused on Perceiver's Age & Habitant- (화장색 이미지평가와 선호도 차이 (제2보) -지각자의 연령과 거주지를 중심으로-)

  • Lee Yon-Hee
    • Journal of the Korean Society of Clothing and Textiles
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    • v.30 no.5 s.153
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    • pp.684-698
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    • 2006
  • This study consists of the stimuli of a female model in her twenties with twenty-two different facial make-up. The subjects of this study are one thousand low hundred ninety seven purposive sampled-male and female grown-ups throughout the country. The period of the research was the December of 2004, one month, and the materials were analyzed by factor analysis, T-examination, analysis of variance, Cronbach's a, Duncan's Multiple Range Test. Here follows the result of the research. Firstly, Familiarity, Intelligence, Fitness, Charm, Tradition and Youth were came out as the result of factor analysis of make-up color image perception. Secondly, in age/lip color perception of bright skin tone, there was difference of Intelligence and Charm. In age/image make-up perception of bright skin tone, there was difference of Familiarity, Charm especially on Cool image make-up. Thirdly in habitant/lip color perception of dark skin tone, there was difference of Intelligence and Charm. In habitant/image make-up perception of bright skin tone, there was difference of Familiarity, Charm and of bright skin tone, Intelligence, Charm, Tradition and Youth. Fourthly, there were the interaction effects on the gender of perceivers and lip color and image make-up of perceivers habitant. Lastly, in preference rate, lip color was more affected by age and image make-up were more affected by perceivers habitant.

Galactic Globular and Open Clusters in the Sloan Digital Sky Survey. III. Horizontal Branch Stars and Mass Loss in NGC 6791

  • Yu, Hyein;An, Deokkeun;Chung, Chul
    • The Bulletin of The Korean Astronomical Society
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    • v.39 no.1
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    • pp.61.2-61.2
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    • 2014
  • We present a set of fiducial sequences of horizontal-branch stars in bright Galactic globular clusters, which have previously been observed in the Sloan Digital Sky Survey (SDSS). We derive fiducial lines on color-magnitude diagrams in multiple color indices (g - r, g - i, g - z, and u - g), after rejecting foreground and background objects as well as RR Lyrae variables utilizing these color indices. We compare our fiducial sequences with model predictions from Yonsei-Yale evolutionary tracks and BaSel spectral libraries, and find a satisfactory agreement between them in terms of their color-magnitude relations, except in u - g. We also compare theoretical models to color-magnitude diagrams of two open clusters (M67 and NGC 6791). Based on our best available cluster distance and reddening, we find that the mass of red clump (RC) stars in NGC 6791 is about a factor of two smaller than an earlier estimate from the application of asteroseismic scaling relations for solar-like oscillations. The smaller RC mass implies an enhanced mass loss along the red giant branch, which is in accordance with other compelling evidences found in this metal-rich system. Our estimated luminosity of RC stars in NGC 6791 is about 0.2 mag fainter than in earlier investigations based on solar-metallicity calibrations, and results in ~10% reduction in the RC-based distance estimation, when applied to metal-rich systems such as in the Galactic bulge.

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Real-Time Human Tracking Using Skin Area and Modified Multi-CAMShift Algorithm (피부색과 변형된 다중 CAMShift 알고리즘을 이용한 실시간 휴먼 트래킹)

  • Min, Jae-Hong;Kim, In-Gyu;Baek, Joong-Hwan
    • Journal of Advanced Navigation Technology
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    • v.15 no.6
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    • pp.1132-1137
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    • 2011
  • In this paper, we propose Modified Multi CAMShift Algorithm(Modified Multi Continuously Adaptive Mean Shift Algorithm) that extracts skin color area and tracks several human body parts for real-time human tracking system. Skin color area is extracted by filtering input image in predefined RGB value range. These areas are initial search windows of hands and face for tracking. Gaussian background model prevents search window expending because it restricts skin color area. Also when occluding between these areas, we give more weights in occlusion area and move mass center of target area in color probability distribution image. As result, the proposed algorithm performs better than the original CAMShift approach in multiple object tracking and even when occluding of objects with similar colors.

Generation of 3 Dimensional Image Model from Multiple Digital Photographs (다중 디지털 사진을 이용한 3차원 이미지 모델 생성)

  • 정태은;석정민;신효철;류재평
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2003.06a
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    • pp.1634-1637
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    • 2003
  • Any given object on the motor-driven turntable is pictured from 8 to 72 different views with a digital camera. 3D shape reconstruction is performed with the integrated software called by Scanware from these multiple digital photographs. There are several steps such as configuration, calibration, capturing, segmentation, shape creation, texturing and merging process during the shape reconstruction process. 3D geometry data can be exported to cad data such as Autocad input file. Also 3D image model is generated from 3D geometry and texture data, and is used to advertise the model in the internet environment. Consumers can see the object realistically from wanted views by rotating or zooming in the internet browsers with Scanbull spx plug-in. The spx format allows a compact saving of 3D objects to handle or download. There are many types of scan equipments such as laser scanners and photogrammetric scanners. Line or point scan methods by laser can generate precise 3D geometry but cannot obtain color textures in general. Reversely, 3D image modeling with photogrammetry can generate not only geometries but also textures from associated polygons. We got various 3D image models and introduced the process of getting 3D image model of an internet-connected watchdog robot.

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Ensemble Deep Learning Features for Real-World Image Steganalysis

  • Zhou, Ziling;Tan, Shunquan;Zeng, Jishen;Chen, Han;Hong, Shaobin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.11
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    • pp.4557-4572
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    • 2020
  • The Alaska competition provides an opportunity to study the practical problems of real-world steganalysis. Participants are required to solve steganalysis involving various embedding schemes, inconsistency JPEG Quality Factor and various processing pipelines. In this paper, we propose a method to ensemble multiple deep learning steganalyzers. We select SRNet and RESDET as our base models. Then we design a three-layers model ensemble network to fuse these base models and output the final prediction. By separating the three colors channels for base model training and feature replacement strategy instead of simply merging features, the performance of the model ensemble is greatly improved. The proposed method won second place in the Alaska 1 competition in the end.

Color Image Segmentation Based on Edge Salience Map and Region Merging (경계 중요도 맵 및 영역 병합에 기반한 칼라 영상 분할)

  • Kim, Sung-Young
    • Journal of the Korea Society of Computer and Information
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    • v.12 no.3
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    • pp.105-113
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    • 2007
  • In this paper, an image segmentation method which is based on edge salience map and region merging is presented. The edge salience map is calculated by combining a texture edge map with a color edge map. The texture edge map is computed over multiple spatial orientations and frequencies by using Gabor filter. A color edge is computed over the H component of the HSI color model. Then the Watershed transformation technique is applied to the edge salience map to and homogeneous regions where the dissimilarity of color and texture distribution is relatively low. The Watershed transformation tends to over-segment images. To merge the over-segmented regions, first of all, morphological operation is applied to the edge salience map to enhance a contrast of it and also to find mark regions. Then the region characteristics, a Gabor texture vector and a mean color, in the segmented regions is defined and regions that have the similar characteristics, are merged. Experimental results have demonstrated the superiority in segmentation results for various images.

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Object-based Image Classification by Integrating Multiple Classes in Hue Channel Images (Hue 채널 영상의 다중 클래스 결합을 이용한 객체 기반 영상 분류)

  • Ye, Chul-Soo
    • Korean Journal of Remote Sensing
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    • v.37 no.6_3
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    • pp.2011-2025
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    • 2021
  • In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.

Object Cataloging Using Heterogeneous Local Features for Image Retrieval

  • Islam, Mohammad Khairul;Jahan, Farah;Baek, Joong Hwan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.11
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    • pp.4534-4555
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    • 2015
  • We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.