• Title/Summary/Keyword: pixel intensity

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Efficient Edge Detection in Noisy Images using Robust Rank-Order Test (잡음영상에서 로버스트 순위-순서 검정을 이용한 효과적인 에지검출)

  • Lim, Dong-Hoon
    • The Korean Journal of Applied Statistics
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    • v.20 no.1
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    • pp.147-157
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    • 2007
  • Edge detection has been widely used in computer vision and image processing. We describe a new edge detector based on the robust rank-order test which is a useful alternative to Wilcoxon test. Our method is based on detecting pixel intensity changes between two neighborhoods with a $r{\times}r$ window using an edge-height model to perform effectively on noisy images. Some experiments of our robust rank-order detector with several existing edge detectors are carried out on both synthetic images and real images with and without noise.

A Shadow Region Suppression Method using Intensity Projection and Converting Energy to Improve the Performance of Probabilistic Background Subtraction (확률기반 배경제거 기법의 향상을 위한 밝기 사영 및 변환에너지 기반 그림자 영역 제거 방법)

  • Hwang, Soon-Min;Kang, Dong-Joong
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.1
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    • pp.69-76
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    • 2010
  • The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

PBR(Physically based Render) simulation considered mathematical Fresnel model for Game Improvement (효율적 게임개선을 위한 프레넬수학모델의 PBR 시뮬레이션)

  • Kim, Seongdong
    • Journal of Korea Game Society
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    • v.16 no.1
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    • pp.111-118
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    • 2016
  • This paper proposes the mathematical model of Fresnel effect used to illuminate and simulate a surface character model for defense game play. The term illumination is used to represent the process by which the amount of light reaching a surface character model used on game play is determined. The character surface shaders generally use a mathematical model to predict how light will reflect on triangles. The shading normally represents the methods used to determine the color and intensity of light reflected toward the viewer for each pixel representing the character surface model of the game. This model computes the reflection and transmission coefficients and compares simulated results to the Fresnel equations for the real game improvement.

Determination of the Proper Block Size for Estimating the Fractal Dimension (프락탈 디멘션을 근사하기 위한 적당한 브록 크기 결정에 관한 연구)

  • Jang, Jong-Hwan
    • The Journal of Natural Sciences
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    • v.7
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    • pp.67-73
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    • 1995
  • In this paper, a new texture segmentation-based image coding technique which performs segmentation based on properties of the human visual system (HVS) is presented. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image into texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. It is very important to determine the proper block size for estimating the fractal dimension. Good quality reconstructed images are obtained with about 0.1 to 0.25 bit per pixel (bpp) for many different types of imagery.

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Design of spectrally encoded real-time slit confocal microscopy (파장 코딩된 실시간 슬릿 공초점 현미경의 설계)

  • Kim Jeong-Min;Kang Dong-Kyun;Gweon Dae-Gab
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2005.10a
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    • pp.576-580
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    • 2005
  • New real-time confocal microscopy using spectral encoding technique and slit confocal aperture is proposed and designed. Spectral encoding technique, which encodes one-dimensional spatial information of a specimen in wavelength, and slit aperture make it possible to obtain two-dimensional lateral image of the specimen simultaneously at standard video rates without expensive scanning units such as polygon mirrors and galvano mirrors. The working principle and the configuration of the system are explained. The variation in axial responses for the simplified model of the system with normalized slit width is numerically analyzed based on the wave optics theory. Slit width that directly affects the depth discrimination of the system is determined by a compromise between axial resolution and signal intensity from the simulation result. On the assumption of the lateral sampling resolution of 50 nm, design variables and governing equations of the system are derived. The system is designed to have the mapping error less than the half pixel size, to be diffraction-limited and to have the maximum illumination efficiency. The designed system has the FOV of $12.8um{\times}9.6um$, the theoretical axial FWHM of 1.1 um and the lateral magnification of-367.8.

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Shallow landslide susceptibility mapping using TRIGRS

  • Viet, Tran The;Lee, Giha;An, Hyun Uk
    • Proceedings of the Korea Water Resources Association Conference
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    • 2015.05a
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    • pp.214-214
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    • 2015
  • Rainfall induced landslides is one of the most devastating natural disasters acting on mountainous areas. In Korea, landslide damage areas increase significantly from 1990s to 2000s due to the increase of both rainfall intensity and rainy days in addition with haphazard land development. This study was carried out based on the application of TRIGRS unsaturated (Transient Rainfall Infiltration and Grid-based Regional Slope stability analysis), a Fortran coded, physically based, and numerical model that can predict landslides for areas where are prone to shallow precipitation. Using TRIGRS combining with the geographic information system (GIS) framework, the landslide incident happened on 27th, July 2011 in Mt. Umyeon in Seoul was modeled. The predicted results which were raster maps showed values of the factors of safety on every pixel at different time steps show a strong agreement with to the observed actual landslide scars in both time and locations. Although some limitations of the program are still needed to be further improved, some soil data as well as landslide information are lack; TRIGRS is proved to be a powerful tool for shallow landslide susceptibility zonation especially in great areas where the input geotechnical and hydraulic data for simulation is not fully available.

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Two-sample Linear Rank Tests for Efficient Edge Detection in Noisy Images (잡음영상에서 효과적인 에지검출을 위한 이표본 선형 순위 검정법)

  • Lim Dong-Hoon
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.4 s.42
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    • pp.9-15
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    • 2006
  • In this paper we propose Wilcoxon test, Median test and Van der Waerden test such as linear rank tests in two-sample location problem for detecting edges effectively in noisy images. These methods are based on detecting image intensity changes between two pixel neighborhoods using an edge-height model to perform effectively on noisy images. The neighborhood size used here is small and its shape is varied adaptively according to edge orientations. We compare and analysis the performance of these statistical edge detectors on both natural images and synthetic images with and without noise.

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Damage Proxy Map (DPM) of the 2016 Gyeongju and 2017 Pohang Earthquakes Using Sentinel-1 Imagery

  • Nur, Arip Syaripudin;Lee, Chang-Wook
    • Korean Journal of Remote Sensing
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    • v.37 no.1
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    • pp.13-22
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    • 2021
  • The ML 5.8 earthquake shocked Gyeongju, Korea, at 11:32:55 UTC on September 12, 2016. One year later, on the afternoon of November 15, 2017, the ML 5.4 earthquake occurred in Pohang, South Korea. The earthquakes injured many residents, damaged buildings, and affected the economy of Gyeongju and Pohang. The damage proxy maps (DPMs) were generated from Sentinel-1 synthetic aperture radar (SAR) imagery by comparing pre- and co-events interferometric coherences to identify anomalous changes that indicate damaged by the earthquakes. DPMs manage to detect coherence loss in residential and commercial areas in both Gyeongju and Pohang earthquakes. We found that our results show a good correlation with the Korea Meteorological Administration (KMA) report with Modified Mercalli Intensity (MMI) scale values of more than VII (seven). The color scale of Sentinel-1 DPMs indicates an increasingly significant change in the area covered by the pixel, delineating collapsed walls and roofs from the official report. The resulting maps can be used to assess the distribution of seismic damage after the Gyeongju and Pohang earthquakes and can also be used as inventory data of damaged buildings to map seismic vulnerability using machine learning in Gyeongju or Pohang.

REAL-TIME 3D MODELING FOR ACCELERATED AND SAFER CONSTRUCTION USING EMERGING TECHNOLOGY

  • Jochen Teizer;Changwan Kim;Frederic Bosche;Carlos H. Caldas;Carl T. Haas
    • International conference on construction engineering and project management
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    • 2005.10a
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    • pp.539-543
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    • 2005
  • The research presented in this paper enables real-time 3D modeling to help make construction processes ultimately faster, more predictable and safer. Initial research efforts used an emerging sensor technology and proved its usefulness in the acquisition of range information for the detection and efficient representation of static and moving objects. Based on the time-of-flight principle, the sensor acquires range and intensity information of each image pixel within the entire sensor's field-of-view in real-time with frequencies of up to 30 Hz. However, real-time working range data processing algorithms need to be developed to rapidly process range information into meaningful 3D computer models. This research ultimately focuses on the application of safer heavy equipment operation. The paper compares (a) a previous research effort in convex hull modeling using sparse range point clouds from a single laser beam range finder, to (b) high-frame rate update Flash LADAR (Laser Detection and Ranging) scanning for complete scene modeling. The presented research will demonstrate if the FlashLADAR technology can play an important role in real-time modeling of infrastructure assets in the near future.

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New Cellular Neural Networks Template for Image Halftoning based on Bayesian Rough Sets

  • Elsayed Radwan;Basem Y. Alkazemi;Ahmed I. Sharaf
    • International Journal of Computer Science & Network Security
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    • v.23 no.4
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    • pp.85-94
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    • 2023
  • Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.