영상처리에 기반한 게이트 운영시스템 개발

Development of Gate Operation System Based on Image Processing

  • 강대성 (동아대학교 공과대학 전기전자컴퓨터공학부) ;
  • 유영달 (동아대학교 대학원 전기전자컴퓨터)
  • 발행 : 1999.12.01

초록

The automated gate operating system is developed in this paper that controls the information of container at gate in the ACT. This system can be divided into three parts and consists of container identifier recognition car plate recognition container deformation perception. We linked each system and organized efficient gate operating system. To recognize container identifier the preprocess using LSPRD(Line Scan Proper Region Detection)is performed and the identifier is recognized by using neural network MBP When car plate is recognized only car image is extracted by using color information of car and hough transform. In the port of container deformation perception firstly background is removed by using moving window. Secondly edge is detected from the image removed characters on the surface of container deformation perception firstly background is removed by using moving window. Secondly edge is detected from the image removed characters on the surface of container. Thirdly edge is fitted into line segment so that container deformation is perceived. As a results of the experiment with this algorithm superior rate of identifier recognition is shown and the car plate recognition system and container deformation perception that are applied in real-time are developed.

키워드

참고문헌

  1. Seminar material An Approach to the Automated Container Terminals Itsuro Watanabe
  2. 韓國港灣學會 v.12 no.2 게이트 자동화를 위한 컨테이너 식별자 인식 시스템 유영달;강대성
  3. Proceedings of the IEEE International Symposium on industrial Electronics v.3 Number plate reading using computer vision J.Barroso;E. L. Dagless;A. Rafael,Bulas-Cruz
  4. ISCAS98 Car Plate by Neural Networks and Image Processing R. Parsi;E.D.Di Claudio
  5. IEEE Computer Society v.3 automatic License Extraction from Moving Vehicles Yuntao Cui;Bian Huang
  6. pattern recognition v.28 no.10 A structural model of shape deformation Hirobumi Nishida
  7. pattern recognition v.28 no.12 Real-time analysis of ships in radar images with neural networks Cesare Alippi
  8. pattern recognition v.31 no.4 A system for model-based object recognition in perspective aerial image Subhodev Das;Bir Bhanu
  9. The Journal of Patterrn Recognition Society v.28 no.1 Machine Printed Charater Segmentation-An Overview Yi Lu
  10. Digital Image Processing Rafael C. Gonzalez;Richard E. Woods
  11. pattern recognition v.30 no.9 Model indexing and object recognition using 3D viewpoint invariance M. Umasuthan