• 제목/요약/키워드: intelligent vision

검색결과 463건 처리시간 0.026초

A Possibilistic C-Means Approach to the Hough Transform for Line Detection

  • Frank Chung-HoonRhee;Shim, Eun-A
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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    • pp.476-479
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    • 2003
  • The Rough transform (HT) is often used for extracting global features in binary images, for example curve and line segments, from local features such as single pixels. The HT is useful due to its insensitivity to missing edge points and occlusions, and robustness in noisy images. However, it possesses some disadvantages, such as time and memory consumption due to the number of input data and the selection of an optimal and efficient resolution of the accumulator space can be difficult. Another problem of the HT is in the difficulty of peak detection due to the discrete nature of the image space and the round off in estimation. In order to resolve the problem mentioned above, a possibilistic C-means approach to clustering [1] is used to cluster neighboring peaks. Several experimental results are given.

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커널 기반의 퍼지 K-Nearest Neighbor 알고리즘 (Fuzzy K-Nearest Neighbor Algorithm based on Kernel Method)

  • 최병인;이정훈
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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    • pp.267-270
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    • 2005
  • 커널 함수는 데이터를 high dimension 상의 속성 공간으로 mapping함으로써 복잡한 분포를 가지는 데이터에 대하여 기존의 선형 분류 알고리즘들의 성능을 향상시킬 수 있다. 본 논문에서는 기존의 유클리디안 거리측정방법 대신에 커널 함수에 의한 속성 공간의 거리측정방법을 fuzzy K-nearest neighbor 알고리즘에 적용한 fuzzy kernel K-nearest neighbor(FKKNN) 알고리즘을 제안한다. 제시한 알고리즘은 데이터에 대한 적절한 커널 함수의 선택으로 기존 알고리즘의 성능을 향상 시킬 수 있다. 제시한 알고리즘의 타당성을 보이기 위하여 여러 데이터 집합에 대한 실험결과를 분석한다.

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IMAGE PROCESSING TECHNIQUES FOR LANE-RELATED INFORMATION EXTRACTION AND MULTI-VEHICLE DETECTION IN INTELLIGENT HIGHWAY VEHICLES

  • Wu, Y.J.;Lian, F.L.;Huang, C.P.;Chang, T.H.
    • International Journal of Automotive Technology
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    • 제8권4호
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    • pp.513-520
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    • 2007
  • In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

A Study on Development of Visual Navigation System based on Neural Network Learning

  • Shin, Suk-Young;Lee, Jang-Hee;You, Yang-Jun;Kang, Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제2권1호
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    • pp.1-8
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    • 2002
  • It has been integrated into several navigation systems. This paper shows that system recognizes difficult indoor roads without any specific marks such as painted guide line or tape. In this method the robot navigates with visual sensors, which uses visual information to navigate itself along the read. The Neural Network System was used to learn driving pattern and decide where to move. In this paper, I will present a vision-based process for AMR(Autonomous Mobile Robot) that is able to navigate on the indoor read with simple computation. We used a single USB-type web camera to construct smaller and cheaper navigation system instead of expensive CCD camera.

Lateral Control of Autonomous Vehicle by Yaw Rate Feedback

  • Yoo, Wan-Suk;Park, Ju-Yong;Hong, Seong-Jae;Park, Kyoung-Taik;Lee, Man-Hyung
    • Journal of Mechanical Science and Technology
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    • 제16권3호
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    • pp.338-343
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    • 2002
  • In the autonomous vehicle, the reference lane is continually detected by machine vision system. And then the vehicle is steered to follow the reference yaw rates which are generated by the deviations of lateral distance and the yaw angle between a vehicle and the reference lane. To cope with the steering delay and the side-slip of vehicle, PI controller is introduced by yaw rate feedback and tuned from the simulation where the vehicle is modeled as 2 DOF and 79 DOF and verified by the results of an actual vehicle test. The lateral control algorithm by yaw rate feedback has good performances of lane tracking and passenger comfort.

Lane Detection for Parking Violation Assessments

  • Kim, A-Ram;Rhee, Sang-Yong;Jang, Hyeon-Woong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제16권1호
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    • pp.13-20
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    • 2016
  • In this study, we propose a method to regulate parking violations using computer vision technology. A still color image of the parked vehicle under question is obtained by a camera mounted on enforcement vehicles. The acquired image is preprocessed through a morphological algorithm and binarized. The vehicle's shadows are detected from the binarized image, and lanes are identified using the information from the yellow parking lines that are drawn on the load. Whether parking is illegal is determined by the conformity of the lanes and the vehicle's shadow.

임베디드 비전 시스템 기반 휴머노이드 로봇의 운동 계획 (Motion Planning for Humanoid Robot Using Embedded Vision System)

  • 노수희;한남이;노흥식;김용태
    • 한국지능시스템학회:학술대회논문집
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    • 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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    • pp.50-53
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    • 2008
  • 본 논문에서는 복잡한 환경에서 휴머노이드 로봇의 영상기반 운동계획을 제안하였다. 먼저 영상전처리 과정을 통해 작업환경에서 경로 계획으로 최적 경로를 탐색하고, 탐색된 경로의 거리와 방향각에 따라 퍼지규칙을 적용하여 보행 프리미티브를 선택하는 운동계획방법을 제안하였다. 다양한 장애물을 갖는 복잡한 환경에서 로봇의 보행 프리미티브를 사용하여 영상기반의 운동계획이 실시간으로 수행 가능하도록 설계하였다. 제안한 운동계획방법은 임베디드 비전 시스템을 사용한 휴머노이드 로봇을 실제 제작하여 실험을 통해 성능을 검증하였다.

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다중카메라와 레이저스캐너를 이용한 확장칼만필터 기반의 노면인식방법 (Road Recognition based Extended Kalman Filter with Multi-Camera and LRF)

  • 변재민;조용석;김성훈
    • 로봇학회논문지
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    • 제6권2호
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    • pp.182-188
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    • 2011
  • This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

초소형 초음파 선형 모터의 조립 자동화를 위한 지능형 민첩 생산시스템 (Agile and Intelligent Manufacturing System for Automatic Assembly of a Tiny Ultrasonic Actuator)

  • 김원;강희석;조영준;이규봉;정지영;서일홍
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 2006년도 춘계학술대회 논문집
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    • pp.607-608
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    • 2006
  • This article deals the development of Agile and Intelligent Manufacturing System(AIM) for the assembly automation of a tiny ultrasonic actuator used in camera phones and PDAs. The system consists of multi-vision modules, end-effectors, a standard base frame, dispensers, jigs and modular manipulators. Subsystems are a vision system, a force control system and a virtual reality system. Experimental results show that the assembly process for the small components in the various IT applications can be realized by the AIM system.

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자동차 추돌경보 시스템 개발을 위한 컴퓨터 비젼과 레이저 레이다의 응용 (An Application of Computer Vision and Laser Radar to a Collision Warning System)

  • 이준웅
    • 한국자동차공학회논문집
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    • 제7권5호
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    • pp.258-267
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    • 1999
  • An intelligent safety vehicle(ISV) should have an ability to predict the possibility of an accident and help a driver avoid the accident in advance. The basic function of the ISV is to alert the driver by warning when the collision is to occur. For this purpose, the ISV has to function efficiently in sensing the environmental context. While image processing provides lane information, laser radar senses road obstacles including vehicles. By applying a simple clustering algorithm to radar signals, it is possible to obtain the vehicle information. Consequently, we can identify the existence of the vehicle of interest on my lane. The reliability of the sensing algorithm is evaluated by running on the highway with a test vehicle.

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