• Title/Summary/Keyword: color vector

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A method of density scalability using SHVC codec in Video based Point Cloud Compression (SHVC 기반 V-PCC 3 차원 포인트 밀도 확장성 지원 방안)

  • Hwang, Yonghae;Kim, Junsik;Kim, Kyuheon
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2020.07a
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    • pp.505-509
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    • 2020
  • 포인트 클라우드 콘텐츠는 3 차원 공간에 수십만 개가 넘는 점들의 집합으로 이루어진 3D 데이터로 각 점들은 3 차원 공간의 좌표 데이터를 필요로 하고 추가적으로 색 (color), 반사율 (reflectance), 법선 벡터 (normal vector) 등과 같은 속성으로 구성되어 있다. 기존 2D 영상보다 한단계 높은 차원을 가진 3D 포인트 클라우드를 사용자에게 효율적으로 제공하기 위해서 고효율의 압축 기술 연구가 진행되고 있는데, 다양한 장치에서 발생하는 성능 차이에 구애 받지 않고 사용자에게 알맞은 서비스를 제공하기 위해서는 다양한 확장성에 대한 연구가 필요하다. 이에 본 논문에서는 포인트 클라우드 압축에 사용되는 Video-based Point Cloud Compression (V-PCC) 구조에 SHVC 코덱을 적용하여, 밀도 확장성을 갖는 포인트 클라우드 압축 비트스트림을 생성하는 방안을 제안하였다.

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Research on Pattern Elements and Colors in Apparel Design through Fractal Theory

  • Dan Li;Chengjun Yuan
    • Journal of Information Processing Systems
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    • v.20 no.3
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    • pp.409-417
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    • 2024
  • Excellent apparel design can increase market competitiveness. This article briefly introduced the theory of fractals and its application in the field of apparel design. The convolutional neural network (CNN) algorithm was used to assist in the evaluation of apparel designs. In the case analysis, the accuracy of the evaluation was validated by comparing the CNN algorithm with two other intelligent algorithms, support vector machine (SVM) and back propagation (BP). The evaluation of the proposed design showed that compared with SVM and BP algorithms, the CNN algorithm had higher accuracy in evaluating apparel designs. The evaluation result of the proposed apparel design not only further verifies the effectiveness of the CNN algorithm, but also demonstrates that the theory of fractals can be effectively applied in apparel design to provide more innovative designs.

Automatic Drawing and Structural Editing of Road Lane Markings for High-Definition Road Maps (정밀도로지도 제작을 위한 도로 노면선 표시의 자동 도화 및 구조화)

  • Choi, In Ha;Kim, Eui Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.6
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    • pp.363-369
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    • 2021
  • High-definition road maps are used as the basic infrastructure for autonomous vehicles, so the latest road information must be quickly reflected. However, the current drawing and structural editing process of high-definition road maps are manually performed. In addition, it takes the longest time to generate road lanes, which are the main construction targets. In this study, the point cloud of the road lane markings, in which color types(white, blue, and yellow) were predicted through the PointNet model pre-trained in previous studies, were used as input data. Based on the point cloud, this study proposed a methodology for automatically drawing and structural editing of the layer of road lane markings. To verify the usability of the 3D vector data constructed through the proposed methodology, the accuracy was analyzed according to the quality inspection criteria of high-definition road maps. In the positional accuracy test of the vector data, the RMSE (Root Mean Square Error) for horizontal and vertical errors were within 0.1m to verify suitability. In the structural editing accuracy test of the vector data, the structural editing accuracy of the road lane markings type and kind were 88.235%, respectively, and the usability was verified. Therefore, it was found that the methodology proposed in this study can efficiently construct vector data of road lanes for high-definition road maps.

The Method of Wet Road Surface Condition Detection With Image Processing at Night (영상처리기반 야간 젖은 노면 판별을 위한 방법론)

  • KIM, Youngmin;BAIK, Namcheol
    • Journal of Korean Society of Transportation
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    • v.33 no.3
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    • pp.284-293
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    • 2015
  • The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

Albedo Based Fake Face Detection (빛의 반사량 측정을 통한 가면 착용 위변조 얼굴 검출)

  • Kim, Young-Shin;Na, Jae-Keun;Yoon, Sung-Beak;Yi, June-Ho
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.45 no.6
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    • pp.139-146
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    • 2008
  • Masked fake face detection using ordinary visible images is a formidable task when the mask is accurately made with special makeup. Considering recent advances in special makeup technology, a reliable solution to detect masked fake faces is essential to the development of a complete face recognition system. This research proposes a method for masked fake face detection that exploits reflectance disparity due to object material and its surface color. First, we have shown that measuring of albedo can be simplified to radiance measurement when a practical face recognition system is deployed under the user-cooperative environment. This enables us to obtain albedo just by grey values in the image captured. Second, we have found that 850nm infrared light is effective to discriminate between facial skin and mask material using reflectance disparity. On the other hand, 650nm visible light is known to be suitable for distinguishing different facial skin colors between ethnic groups. We use a 2D vector consisting of radiance measurements under 850nm and 659nm illumination as a feature vector. Facial skin and mask material show linearly separable distributions in the feature space. By employing FIB, we have achieved 97.8% accuracy in fake face detection. Our method is applicable to faces of different skin colors, and can be easily implemented into commercial face recognition systems.

Optical Multi-Normal Vector Based Iridescence BRDF Compression Method (광학적 다중 법선 벡터 기반 훈색(暈色)현상 BRDF 압축 기법)

  • Ryu, Sae-Woon;Lee, Sang-Hwa;Park, Jong-Il
    • Journal of KIISE:Computer Systems and Theory
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    • v.37 no.3
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    • pp.184-193
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    • 2010
  • This paper proposes a biological iridescence BRDF(Bidirectional Reflectance Distribution Function) compression and rendering method. In the graphics technology, iridescence sometimes is named structure colors. The main features of these symptoms are shown transform of color and brightness by varying viewpoint. Graphics technology to render this is the BRDF technology. The BRDF methods enable realistic representation of varying view direction, but it requires a lot of computing power because of large data. In this paper, we obtain reflection map from iridescence BRDF, analyze color of reflection map and propose representation method by several colorfully concentric circle. The one concentric circle represents beam width of reflection ray by one normal vector. In this paper, we synthesize rough concentric by using several virtually optical normal vectors. And we obtain spectrum information from concentric circles passing through the center point. The proposed method enables IBR(image based rendering) technique which results is realistic illuminance and spectrum distribution by one texture from reduced BRDF data within spectrum.

Agrobacterium-Mediated Transformation on a Plant with Saccharomyces cerevisiae Acid Phosphatse Gene(PHO5) (Agrobacterium을 이용한 Saccharomyces cerevisiae Acid Phosphatse 유전자 (PHO5) 의 식물체로의 도입)

  • Ki yong Kim;Dae yuong Son;Yong Gu Park;Won Il Jung;Jin Ki Jo
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.13 no.3
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    • pp.177-183
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    • 1993
  • This study was conducted to obtain the transformed tobacco plants with S. cerevisiae Acid phosphatase gene(PH05) using Agrobacterium tumefaciens and th confirm plant transformation and gene expression. the results obtained were summarized as follows: APase activity of Saccharomyces cereviase NA 87-11A was remarkably showed up as deep red color when assayed by Tohe and Oshima(1974). PH05 fragment, Apase gene, was obtained from pVC727G and the graphically estimated size was about 1.5kb by agarose gel electrophoresis. The sequencing results of 5'end and 3'end of PH05 using dideoxy chain termination method were coinsided with the full length nucleotide already. pBKJ I vector was constructed by isolation of PH05 fragment from pVC727-1 and pBKSI-1 digesred with Sma I and Xba I. Isolated plasmid from transformed A. tumefaciens with constructed pBKJ I when it was electrophoresed with agarose gel. The dosc of tobacco leaf was cocultivated 재소 transformed Agronacterium tumefaciens. Transformed shoots were selected on kanamtcin-containing MS-n/B medium and they were regenerated. The transgenic tobacco plants were elucidated by isolation of genomic DNA and genomic southern hybridization using ${\alpha}-^{32}P$ labelled PH05 fragments. The PH05 in transformed tobacco plants was expressed in leaf, stem and root, and its APase activity was estimated as deep red color by Tohe method.

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Study of a Recurring Anticyclonic Eddy off Wonsan Coast in Northern Korea Using Satellite Tracking Drifter, Satellite Ocean Color and Sea Surface Temperature Imagery (위성원격탐사를 이용한 동해 원산연안의 재발생 와동류 연구)

  • 서영상;장이현;김정희
    • Korean Journal of Remote Sensing
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    • v.16 no.3
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    • pp.211-220
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    • 2000
  • Even though recurring eddies at the terminal end of the East Korean Warm Current have been identified in the thermal infrared imagery from the NOAA/AVHRR sensor and ocean color data from Orbview-2/SeaWiFS sensor, it is difficult to make observation in the field regarding recurring eddies located around the Wonsan coastal area in North Korea. But we could get in situ data related to an eddy from an ARGOS satellite tracking drifter trapped in the eddy on January 4th, 1999. An ARGOS drifter, a NOAA satellite tracked buoy was trapped by the eddy during January 4th.March 18, 1999. The ARGOS drifter rotated 10 times per 72 days on the edge of the eddy located at $39^{\circ}N$, $129^{\circ}E$. The diameter of the eddy was about 100 km. The horizontal rotation velocity of the recurring cold-core anti-cyclonic eddy was 1.53 km/h(42 cm/sec). The sea surface temperatures of the eddy varied from $14.7^{\circ}C$ on January 5, 1999 to $9.6^{\circ}C$ on March 18,1999. To study the mechanism of the recurring eddy. we tried to find out the relationship between the vector of the drifter moving in the eddy and the wind vector in Sokcho and Ulleung Island located near the eddy in southern Korea, and the difference in sea level between Ulleung Island and Mukho. We hope the results of this study would be useful for calibration and validation data of simulation and numerical modeling studies of the recurring eddy.

Object VR-based Virtual Textile Wearing System Using Textile Texture Mapping (직물 텍스쳐 매핑을 이용한 객체 VR 기반 가상 직물 착용 시스템)

  • Kwak, No-Yoon
    • Journal of Digital Convergence
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    • v.10 no.8
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    • pp.239-247
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    • 2012
  • This paper is related to an Object VR-based virtual textile wearing system carrying out textile texture mapping based on viewpoint vector estimation and intensity difference map. The proposed system is characterized as capable of virtually wearing a new textile pattern selected by the user to the clothing shape section segmented from multi-view 2D images of clothes model for Object VR(Object Virtual Reality), and three-dimensionally viewing its virtual wearing appearance at multi-view points of the object. Regardless of color or intensity of model clothes, the proposed system is possible to virtually change the textile pattern with holding the properties of the selected clothing shape section, and also to quickly and easily simulate, compare, and select multiple textile pattern combinations for individual styles or entire outfits. The proposed system can provide higher practicality and easy-to-use interface, as it makes real-time processing possible in various digital environment, and creates comparatively natural and realistic virtual wearing styles, and also makes semi-automatic processing possible to reduce the manual works.

Medical Image Automatic Annotation Using Multi-class SVM and Annotation Code Array (다중 클래스 SVM과 주석 코드 배열을 이용한 의료 영상 자동 주석 생성)

  • Park, Ki-Hee;Ko, Byoung-Chul;Nam, Jae-Yeal
    • The KIPS Transactions:PartB
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    • v.16B no.4
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    • pp.281-288
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    • 2009
  • This paper proposes a novel algorithm for the efficient classification and annotation of medical images, especially X-ray images. Since X-ray images have a bright foreground against a dark background, we need to extract the different visual descriptors compare with general nature images. In this paper, a Color Structure Descriptor (CSD) based on Harris Corner Detector is only extracted from salient points, and an Edge Histogram Descriptor (EHD) used for a textual feature of image. These two feature vectors are then applied to a multi-class Support Vector Machine (SVM), respectively, to classify images into one of 20 categories. Finally, an image has the Annotation Code Array based on the pre-defined hierarchical relations of categories and priority code order, which is given the several optimal keywords by the Annotation Code Array. Our experiments show that our annotation results have better annotation performance when compared to other method.