• Title/Summary/Keyword: 다중색상 모델

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Deep Learning-based Mango Classification and Prediction System of Fruit Ripening using YOLO (딥러닝기반 YOLO를 활용한 후숙과일 분류 및 숙성 예측 시스템)

  • Kim, Yeong-Min;Park, Seung-Min
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.187-188
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    • 2021
  • 본 논문에서는 실시간으로 web-cam을 이용해, 후숙과일의 불량 여부를 판단, 분류하고 불량이 없는 후숙과일의 이미지 분석을 통하여 숙성도 예측하는 시스템을 소개한다. 실시간 다중 객체인식에 탁월한 yolo모델을 활용해, 과일의 불량여부 판단 후 분류하고, 이미지를 획득한 뒤, k-mean clustering 알고리즘을 이용해, 이미지를 segmentation 한다. segmentation된 이미지에 grabcut 알고리즘의 foreground-extraction을 사용해 배경 제거를 한 뒤, cluster의 중심색상값 색상값의 면적%, 전체 면적을 이용해 현재 숙성도를 계산하고 이를 이용해 과일의 후숙 시간 데이터와 비교, 숙성이 완료될 시간을 예측한다. 기존 수작업으로 이루어지고 있는 과일의 분류작업의 인력 감소 및 정확성을 높일 수 있는 알고리즘을 제안한다.

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The Method for Colorizing SAR Images of Kompsat-5 Using Cycle GAN with Multi-scale Discriminators (다양한 크기의 식별자를 적용한 Cycle GAN을 이용한 다목적실용위성 5호 SAR 영상 색상 구현 방법)

  • Ku, Wonhoe;Chun, Daewon
    • Korean Journal of Remote Sensing
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    • v.34 no.6_3
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    • pp.1415-1425
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    • 2018
  • Kompsat-5 is the first Earth Observation Satellite which is equipped with an SAR in Korea. SAR images are generated by receiving signals reflected from an object by microwaves emitted from a SAR antenna. Because the wavelengths of microwaves are longer than the size of particles in the atmosphere, it can penetrate clouds and fog, and high-resolution images can be obtained without distinction between day and night. However, there is no color information in SAR images. To overcome these limitations of SAR images, colorization of SAR images using Cycle GAN, a deep learning model developed for domain translation, was conducted. Training of Cycle GAN is unstable due to the unsupervised learning based on unpaired dataset. Therefore, we proposed MS Cycle GAN applying multi-scale discriminator to solve the training instability of Cycle GAN and to improve the performance of colorization in this paper. To compare colorization performance of MS Cycle GAN and Cycle GAN, generated images by both models were compared qualitatively and quantitatively. Training Cycle GAN with multi-scale discriminator shows the losses of generators and discriminators are significantly reduced compared to the conventional Cycle GAN, and we identified that generated images by MS Cycle GAN are well-matched with the characteristics of regions such as leaves, rivers, and land.

Visual Tracking Using Monte Carlo Sampling and Background Subtraction (확률적 표본화와 배경 차분을 이용한 비디오 객체 추적)

  • Kim, Hyun-Cheol;Paik, Joon-Ki
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.5
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    • pp.16-22
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    • 2011
  • This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

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.

Robust 3D Hand Tracking based on a Coupled Particle Filter (결합된 파티클 필터에 기반한 강인한 3차원 손 추적)

  • Ahn, Woo-Seok;Suk, Heung-Il;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.37 no.1
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    • pp.80-84
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    • 2010
  • Tracking hands is an essential technique for hand gesture recognition which is an efficient way in Human Computer Interaction (HCI). Recently, many researchers have focused on hands tracking using a 3D hand model and showed robust tracking results compared to using 2D hand models. In this paper, we propose a novel 3D hand tracking method based on a coupled particle filter. This provides robust and fast tracking results by estimating each part of global hand poses and local finger motions separately and then utilizing the estimated results as a prior for each other. Furthermore, in order to improve the robustness, we apply a multi-cue based method by integrating a color-based area matching method and an edge-based distance matching method. In our experiments, the proposed method showed robust tracking results for complex hand motions in a cluttered background.

Effective Morphological Layer Segmentation Based on Edge Information for Screen Image Coding (스크린 이미지 부호화를 위한 에지 정보 기반의 효과적인 형태학적 레이어 분할)

  • Park, Sang-Hyo;Lee, Si-Woong
    • The Journal of the Korea Contents Association
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    • v.13 no.12
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    • pp.38-47
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    • 2013
  • An image coding based on MRC model, a kind of multi-layer image model, first segments a screen image into foreground, mask, and background layers, and then compresses each layer using a codec that is suitable to the layer. The mask layer defines the position of foreground regions such as textual and graphical contents. The colour signal of the foreground (background) region is saved in the foreground (background) layer. The mask layer which contains the segmentation result of foreground and background regions is of importance since its accuracy directly affects the overall coding performance of the codec. This paper proposes a new layer segmentation algorithm for the MRC based image coding. The proposed method extracts text pixels from the background using morphological top hat filtering. The application of white or black top hat transformation to local blocks is controlled by the information of relative brightness of text compared to the background. In the proposed method, the boundary information of text that is extracted from the edge map of the block is used for the robust decision on the relative brightness of text. Simulation results show that the proposed method is superior to the conventional methods.

Image Segmentation for Fire Prediction using Deep Learning (딥러닝을 이용한 화재 발생 예측 이미지 분할)

  • TaeHoon, Kim;JongJin, Park
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.23 no.1
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    • pp.65-70
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    • 2023
  • In this paper, we used a deep learning model to detect and segment flame and smoke in real time from fires. To this end, well known U-NET was used to separate and divide the flame and smoke of the fire using multi-class. As a result of learning using the proposed technique, the values of loss error and accuracy are very good at 0.0486 and 0.97996, respectively. The IOU value used in object detection is also very good at 0.849. As a result of predicting fire images that were not used for learning using the learned model, the flame and smoke of fire are well detected and segmented, and smoke color were well distinguished. Proposed method can be used to build fire prediction and detection system.

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.

An Embedded Information Extraction of Color QR Code for Offline Applications (오프라인 응용을 위한 컬러 QR코드의 삽입 정보 추출 방법)

  • Kim, Jin-soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.9
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    • pp.1123-1131
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    • 2020
  • The quick-response (QR) code is a two-dimensional barcode which is widely being used. Due to several interesting features such as small code size, high error correction capabilities, easy code generation and reading process, the QR codes are used in many applications. Nowadays, a printed color QR code for offline applications is being studied to improve the information storage capacity. By multiplexing color information into the conventional black-white QR code, the storage capacity is increased, however, it is hard to extract the embedded information due to the color crosstalk and geometrical distortion. In this paper, to overcome these problems, a new type of QR code is designed based on the CMYK color model and the local spatial searching as well as the global spatial matching is introduced in the reading process. These results in the recognition rate increase. Through practical experiments, it is shown that the proposed algorithm can perform the bit recognition rate improvement of about 3% to 5%.

Multi Scale Tone Mapping Model Using Visual Brightness Functions for HDR Image Compression (HDR 영상 압축을 위한 시각 밝기 함수를 이용한 다중 스케일 톤 맵핑 모델)

  • Kwon, Hyuk-Ju;Lee, Sung-Hak;Chae, Seok-Min;Sohng, Kyu-Ik
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37A no.12
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    • pp.1054-1064
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    • 2012
  • HDR (high dynamic range) tone mapping algorithms are used in image processing that reduces the dynamic range of an image to be displayed on LDR (low dynamic range) devices properly. The retinex is one of the tone mapping algorithms to provide dynamic range compression, color constancy, and color rendition. It has been developed through multi-scale methods and luminance-based methods. Retinex algorithms still have drawbacks such as the emphasized noise and desaturation. In this paper, we propose a multi scale tone mapping algorithm for enhancement of contrast, saturation, and noise of HDR rendered images based on visual brightness functions. In the proposed algorithm, HSV color space has been used for preserving the hue and saturation of images. And the algorithm includes the estimation of minimum and maximum luminance level and a visual gamma function for the variation of viewing conditions. And subjective and objective evaluations show that proposed algorithm is better than existing algorithms. The proposed algorithm is expected to image quality enhancement in some fields that require a improvement of the dynamic range due to the changes in the viewing condition.