• 제목/요약/키워드: local vision

검색결과 382건 처리시간 0.03초

이미지 특징 추출연산자 DBAH/DBAG 와 하드웨어 실현 (Image Feature Extracting Operators Using DBAH/DBAG and its Implementation)

  • Cho, Sung-Mok
    • 한국컴퓨터정보학회논문지
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    • 제6권1호
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    • pp.31-37
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    • 2001
  • 인간의 시각현상은 밝은 영역보다 어두운 영역의 특징점 추출에 더 민감하다. 그러므로 특징점 추출 연산자는 인간의 시각체계와 유사하게 물체를 인식하기 위해 국부적인 밝기를 고려해야 한다. 일반적으로 지금까지의 특징점 추출 연산자는 계산량이 많거나 다변수를 이용해야 하는 문제점을 가지고 있다. 본 논문에서는 이러한 단점을 극복하는 새로운연산자가 제안된다. 이 연산자는 실현이 매우 간단할 뿐만 아니라 합성영상과 실영상에 적용한 결과 우수한 성능을 나타내었다.

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텍셀을 이용한 3차원 물체의 형상 인식 (Shape Recognition of 3-D Object Using Texels)

  • 김도년;조동섭
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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    • pp.460-464
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    • 1990
  • Texture provides an important source of information about the local orientation of visible surfaces. An important task that arises in many computer vision systems is the reconstruction of three-dimensional depth information from two-dimensional images. The surface orientation of texel is classified by the Artificial Neural Network. The classification method to recognize the shape of 3D object with artificial neural network requires less developing time comparing to conventional method. The segmentation problem is assumed to be solved. The surface in view is smooth and is covered with repeated texture elements. In this study, 3D shape reconstruct using interpolation method.

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An algorithm to acquire the reaction area of skin allergy images

  • Kim, In-Soo;Lee, Myong-Gu;Park, Mignon;Lee, Sang-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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    • pp.1748-1751
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    • 1991
  • Generally, we can't acquire clear boundary or area from an image having obscure boundary like allergy image by using Sobel or Lapalace operator. Also, when the image not uniform in some part of a image in brightness, there are difficulties to use the global operator such as histogram, for the contour line doesn't have the same grey level. In this paper, we will propose an algorithm to improve those difficulties. The main idea of the algorithm is that we divide the image into many rectangular parts like a chess board, calculate the average of each part, and decide the local threshold for each pixel on the calculated value. In experiment, we can get the contour and area by this algorithm which is much like to the contour and area measured by a doctor. Also, This algorithm has many advantages such as short processing time and little influences of noises and can be used in the robot vision, etc..

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인간 시각의 칼라 활성 가중 왜곡 척도를 이용한 칼라 영상 양자화 (Color image quantization using color activity weighted distortion measure of human vision)

  • 김경만;이응주;박양우;이채수;하영호
    • 전자공학회논문지B
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    • 제33B권4호
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    • pp.101-110
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    • 1996
  • Color image quantization is a process of selecting a set of colors to display an image with some representative colors without noticeable perceived difference. It is very important in many applications to display a true color image in a low cost color monitor or printer. the basic problem is how to display 224 colors with 256 or less colors, called color palette. In this paper, we propose an algorithm to design the 256 or less size color palette by using spatial maskin geffect of HVS and subjective distortion measure weighted by color palette by using spatial masking effect of HVS and subjective distortion measure weighted by color activity in 4*4 local region in any color image. The proposed algorithm consists of octal prequantization and subdivision quantization processing step using the distortion measure and modified Otsu's between class variance maximization method. The experimental results show that the proposed algorithm has higher visual quality and needs less consuming time than conventional algorithms.

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Visual Bean Inspection Using a Neural Network

  • Kim, Taeho;Yongtae Do
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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    • pp.644-647
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    • 2003
  • This paper describes a neural network based machine vision system designed for inspecting yellow beans in real time. The system consists of a camera. lights, a belt conveyor, air ejectors, and a computer. Beans are conveyed in four lines on a belt and their images are taken by a monochrome line scan camera when they fall down from the belt. Beans are separated easily from their background on images by back-lighting. After analyzing the image, a decision is made by a multilayer artificial neural network (ANN) trained by the error back-propagation (EBP) algorithm. We use the global mean, variance and local change of gray levels of a bean for the input nodes of the network. In an our experiment, the system designed could process about 520kg/hour.

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디지탈 이미지프로세싱을 이용한 자동외관검사장치 개발 (System Development for Automatic Form Inspecion by Digital Image Processing)

  • 유봉환
    • 한국생산제조학회지
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    • 제5권2호
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    • pp.57-62
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    • 1996
  • Basically, the idea underlying most edge-detection technique is the computation of a local derivative operator used for edge detection in gray level image. This concept can be easily illustrated with the aid of object which shows an image of a simple lilght on a dark background, Using the gray level profile along a horizontal scan line of the image. the first and second derivatives of it were acquired. This study is to develop an automatic measuring system based on the digital image processing which can be applied to the real time measurement of the characteristics of the ultra-thin thickness. The experimental results indicate that the developed automatic inspection can be applied in real situation.

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An Improved Stereo Matching Algorithm with Robustness to Noise Based on Adaptive Support Weight

  • Lee, Ingyu;Moon, Byungin
    • Journal of Information Processing Systems
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    • 제13권2호
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    • pp.256-267
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    • 2017
  • An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

아다부스트 학습과 비정방형 Differential LBP를 이용한 얼굴영상 특징분석 (Face Image Analysis using Adaboost Learning and Non-Square Differential LBP)

  • 임길택;원철호
    • 한국멀티미디어학회논문지
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    • 제19권6호
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    • pp.1014-1023
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    • 2016
  • In this study, we presented a method for non-square Differential LBP operation that can well describe the micro pattern in the horizontal and vertical component. We proposed a way to represent a LBP operation with various direction components as well as the diagonal component. In order to verify the validity of the proposed operation, Differential LBP was investigated with respect to accuracy, sensitivity, and specificity for the classification of facial expression. In accuracy comparison proposed LBP operation obtains better results than Square LBP and LBP-CS operations. Also, Proposed Differential LBP gets better results than previous two methods in the sensitivity and specificity indicators 'Neutral', 'Happiness', 'Surprise', and 'Anger' and excellence Differential LBP was confirmed.

Extraction of Geometric Primitives from Point Cloud Data

  • Kim, Sung-Il;Ahn, Sung-Joon
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.2010-2014
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    • 2005
  • Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

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The Edge Distribution Function Based Method of Trajectory Tracking for AGV

  • Yi, Un-Kun;Ha, Sung-Kil;Jung, Sung-Yun;Hwang, Hee-Jung;Baek, Kwang-Ryul
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.1701-1704
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    • 2005
  • We developed an machine vision method for navigation control of a traveling automatic guided vehicle(AGV) on desired trajectory with guided marks. The formulated EDF accumulates the edge magnitude for edge directions. The EDF has distinctive peak points at the vicinity of trajectory directions due to the directional and the positional continuities of desired trajectory. Examining the EDF by its shape parameters of the local maxima and symmetry axis results in identifying whether or not change in traveling direction of an AGV has occurred. Simulation results show that the presented method is useful for navigation control of AGV.

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