• Title/Summary/Keyword: Descent image

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Study on Compass, Carpenter's square, The Beam of Balance and the Weight of balance[規矩權衡] in "Somun(素問).Maekyojeongmiron(脈要精微論)" ("소문(素問).맥요정미론(脈要精微論)"의 규구충권(規矩衡權)에 대한 연구)

  • Lee, Hye-Yeon;Kang, Jung-Soo
    • Journal of Korean Medical classics
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    • v.23 no.1
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    • pp.101-114
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    • 2010
  • In the perspective of the correspondence of heaven and man[天人相應], people live through Gi of heaven and earth[天地之氣], and the human body which is a small universe[小宇宙] itself receives influence while sympathizing with the Gi and heaven[天氣]. So with unexpected incident of the Eum and Yang, four season[陰陽四時], ups and downs of warmth of cold and chilliness of warm[寒熱溫涼] differs, and the position of Gi of human[人氣] changes, regimen and application of acupuncture, and images[象] of the pulse changes. In "Maekyojeongmiron(脈要精微論)", ups and downs of Eum and Yang changes by four season[四時], and correspondence of ups and downs of pulse law is explained with compass, carpenter's square, the beam of balance and the weight of balance[規矩權衡]. Compass[規] is a measure of instrument that can draw a circle, like regulating the measure and differing the center of the circle and diameter and drawing a circle, compass is a image of Gi of Yang[陽氣] that was staying deep inside the body in winter stretching out by big fault[太過不及] of year and energy[元氣] of human in spring. Carpenter's square[矩] is a instrument that draws direction, which is a image of Gi of Yang flourishing in summer and when it gets highly flourished, again the Gi of Eum[陰氣] comes alive and falls. The beam of balance[衡] is a scale, like a scale that tilts at once when one side is slightly heavy, the beam of balance is a image Gi of yang that is fully flourished in summer and about to descent again, which is just about to fall but not going down yet. The weight of balance [權] is a image of gi of yang which as descent to the bottom and staying in the deepest place. compass, carpenter's square, the beam of balance and the weight of balance is not a direct pulse image[脈象], but standard image of pulse of pulse corresponding to the Gi of human[人氣] that changes by four season, and the explanation includes the pulse image of four season like the taut, full, floating, deeply gather[弦鉤浮營] of "Okgijinjangron(玉機眞藏論)" or taut, full, skip, float, deep [弦鉤代毛石] of "Pyeong-ingisangron(平人氣象論)". So with compass, carpenter's square, the beam of balance and the weight of balance, can judge is human correspond in Eum and Yang, four seasons, this is importantly used in examination of pulse[診脈] with existence and nonexistence, and prognosis of illness.

Entropy-Based 6 Degrees of Freedom Extraction for the W-band Synthetic Aperture Radar Image Reconstruction (W-band Synthetic Aperture Radar 영상 복원을 위한 엔트로피 기반의 6 Degrees of Freedom 추출)

  • Hyokbeen Lee;Duk-jin Kim;Junwoo Kim;Juyoung Song
    • Korean Journal of Remote Sensing
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    • v.39 no.6_1
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    • pp.1245-1254
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    • 2023
  • Significant research has been conducted on the W-band synthetic aperture radar (SAR) system that utilizes the 77 GHz frequency modulation continuous wave (FMCW) radar. To reconstruct the high-resolution W-band SAR image, it is necessary to transform the point cloud acquired from the stereo cameras or the LiDAR in the direction of 6 degrees of freedom (DOF) and apply them to the SAR signal processing. However, there are difficulties in matching images due to the different geometric structures of images acquired from different sensors. In this study, we present the method to extract an optimized depth map by obtaining 6 DOF of the point cloud using a gradient descent method based on the entropy of the SAR image. An experiment was conducted to reconstruct a tree, which is a major road environment object, using the constructed W-band SAR system. The SAR image, reconstructed using the entropy-based gradient descent method, showed a decrease of 53.2828 in mean square error and an increase of 0.5529 in the structural similarity index, compared to SAR images reconstructed from radar coordinates.

A study on the realization of color printed material check using Error Back-Propagation rule (오류 역전파법으로구현한 컬러 인쇄물 검사에 관한 연구)

  • 한희석;이규영
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.10a
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    • pp.560-567
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    • 1998
  • This paper concerned about a imputed color printed material image in camera to decrease noise and distortion by processing median filtering with input image to identical condition. Also this paper proposed the way of compares a normal printed material with an abnormal printed material color tone with trained a learning of the error back-propagation to block classification by extracting five place from identical block(3${\times}$3) of color printed material R, G, B value. As a representative algorithm of multi-layer perceptron the error Back-propagation technique used to solve complex problems. However, the Error Back-propagation is algorithm which basically used a gradient descent method which can be converged to local minimum and the Back Propagation train include problems, and that may converge in a local minimum rather than get a global minimum. The network structure appropriate for a given problem. In this paper, a good result is obtained by improve initial condition and adjust th number of hidden layer to solve the problem of real time process, learning and train.

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Development of Visual Servo Control System for the Tracking and Grabbing of Moving Object (이동 물체 포착을 위한 비젼 서보 제어 시스템 개발)

  • Choi, G.J.;Cho, W.S.;Ahn, D.S.
    • Journal of Power System Engineering
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    • v.6 no.1
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    • pp.96-101
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    • 2002
  • In this paper, we address the problem of controlling an end-effector to track and grab a moving target using the visual servoing technique. A visual servo mechanism based on the image-based servoing principle, is proposed by using visual feedback to control an end-effector without calibrated robot and camera models. Firstly, we consider the control problem as a nonlinear least squares optimization and update the joint angles through the Taylor Series Expansion. And to track a moving target in real time, the Jacobian estimation scheme(Dynamic Broyden's Method) is used to estimate the combined robot and image Jacobian. Using this algorithm, we can drive the objective function value to a neighborhood of zero. To show the effectiveness of the proposed algorithm, simulation results for a six degree of freedom robot are presented.

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AN IMAGE SEGMENTATION LEVEL SET METHOD FOR BUILDING DETECTION

  • Konstantinos, Karantzalos;Demetre, Argialas
    • Proceedings of the KSRS Conference
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    • v.2
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    • pp.610-614
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    • 2006
  • In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

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Camera Calibration Using the Fuzzy Model (퍼지 모델을 이용한 카메라 보정에 관한 연구)

  • 박민기
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.5
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    • pp.413-418
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    • 2001
  • In this paper, we propose a new camera calibration method which is based on a fuzzy model instead of a physical camera model of the conventional method. The camera calibration is to determine the correlation between camera image coordinate and real world coordinate. The camera calibration method using a fuzzy model can not estimate camera physical parameters which can be obtained in the conventional methods. However, the proposed method is very simple and efficient because it can determine the correlation between camera image coordinate and real world coordinate without any restriction, which is the objective of camera calibration. With calibration points acquired out of experiments, 3-D real world coordinate and 2-D image coordinate are estimated using the fuzzy modeling method and the results of the experiments demonstrate the validity of the proposed method.

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Wine Label Recognition System using Image Similarity (이미지 유사도를 이용한 와인라벨 인식 시스템)

  • Jung, Jeong-Mun;Yang, Hyung-Jeong;Kim, Soo-Hyung;Lee, Guee-Sang;Kim, Sun-Hee
    • The Journal of the Korea Contents Association
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    • v.11 no.5
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    • pp.125-137
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    • 2011
  • Recently the research on the system using images taken from camera phones as input is actively conducted. This paper proposed a system that shows wine pictures which are similar to the input wine label in order. For the calculation of the similarity of images, the representative color of each cell of the image, the recognized text color, background color and distribution of feature points are used as the features. In order to calculate the difference of the colors, RGB is converted into CIE-Lab and the feature points are extracted by using Harris Corner Detection Algorithm. The weights of representative color of each cell of image, text color and background color are applied. The image similarity is calculated by normalizing the difference of color similarity and distribution of feature points. After calculating the similarity between the input image and the images in the database, the images in Database are shown in the descent order of the similarity so that the effort of users to search for similar wine labels again from the searched result is reduced.

Feature Points Tracking of Digital Image By One-Directional Iterating Layer Snake Model (일방향 순차층위 스네이크 모델에 의한 디지털영상의 특징점 추적)

  • Hwang, Jung-Won;Hwang, Jae-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.44 no.4 s.316
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    • pp.86-92
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    • 2007
  • A discrete dynamic model for tracking feature points in 2D images is developed. Conventional snake approaches deform a contour to lock onto features of interest within an image by finding a minimum of its energy functional, composed of internal and external forces. The neighborhood around center snaxel is a space matrix, typically rectangular. The structure of the model proposed in this paper is a set of connected vertices. Energy model is designed for its local minima to comprise the set of alternative solutions available to active process. Block on tracking is one dimension, line type. Initial starting points are defined to the satisfaction of indent states, which is then automatically modified by an energy minimizing process. The track is influenced by curvature constraints, ascent/descent or upper/lower points. The advantages and effectiveness of this layer approach may also be applied to feature points tracking of digital image whose pixels have one directional properties with high autocorrelation between adjacent data lines, vertically or horizontally. The test image is the ultrasonic carotid artery image of human body, and we have verified its effect on intima/adventitia starting points tracking.

KMTNet Supernova Project : Pipeline and Alerting System Development

  • Lee, Jae-Joon;Moon, Dae-Sik;Kim, Sang Chul;Pak, Mina
    • The Bulletin of The Korean Astronomical Society
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    • v.40 no.1
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    • pp.56.2-56.2
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    • 2015
  • The KMTNet Supernovae Project utilizes the large $2^{\circ}{\times}2^{\circ}$ field of view of the three KMTNet telescopes to search and monitor supernovae, especially early ones, and other optical transients. A key component of the project is to build a data pipeline with a descent latency and an early alerting system that can handle the large volume of the data in an efficient and a prompt way, while minimizing false alarms, which casts a significant challenge to the software development. Here we present the current status of their development. The pipeline utilizes a difference image analysis technique to discover candidate transient sources after making correction of image distortion. In the early phase of the program, final selection of transient sources from candidates will mainly rely on multi-filter, multi-epoch and multi-site screening as well as human inspection, and an interactive web-based system is being developed for this purpose. Eventually, machine learning algorithms, based on the training set collected in the early phase, will be used to select true transient sources from candidates.

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Scene-based Nonuniformity Correction for Neural Network Complemented by Reducing Lense Vignetting Effect and Adaptive Learning rate

  • No, Gun-hyo;Hong, Yong-hee;Park, Jin-ho;Jhee, Ho-jin
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.7
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    • pp.81-90
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    • 2018
  • In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner's neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner's neural network method(NNM) and Torres's reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm.