DOI QR코드

DOI QR Code

영상 이미지의 특정 영역 검출을 위한 정렬 보정 알고리즘 연구

A Study on Alignment Correction Algorithm for Detecting Specific Areas of Video Images

  • Jin, Go-Whan (Division of IT Convergence, Woosong University)
  • 투고 : 2018.09.27
  • 심사 : 2018.11.20
  • 발행 : 2018.11.28

초록

비전 시스템은 영상 이미지를 획득하여 대상 영역을 판별하고 분석하는 시스템이며, 자동화 공정에 사용하고자 하는 수요가 증가하면서 비전 기반의 검사 시스템 도입이 매우 중요한 이슈로 부상하고 있다. 이러한 비전 시스템은 일상생활과 생산 공정에서 검사 장비로 사용되고 있으며, 영상 처리 기술에 대한 연구가 매우 활발하게 이루어지고 있다. 그러나 문자 인식이나 반도체 패키지 등의 검사 대상을 추출하기 위한 영역 정의에 대한 연구는 미미한 상황이다. 본 논문에서는 사용자가 관심영역을 정의하여 엣지 추출을 수행함에 있어 잡음까지도 엣지로 판단하는 경우를 방지하기 위하여, 영상 이미지 내에서 잡음이 존재하여도 특정한 영역의 엣지들의 분포를 이용하여 검사 대상 영역의 엣지를 추출할 수 있는 잡음에 강인한 정렬 보정 모델을 제안한다. 제안 모델을 통하여 타이어의 문자 인식이나 반도체 패키지 검사와 같은 생산 분야에 적용하면 제품의 생산 효율이 향상될 수 있을 것으로 기대된다.

The vision system is a device for acquiring images and analyzing and discriminating inspection areas. Demand for use in the automation process has increased, and the introduction of a vision-based inspection system has emerged as a very important issue. These vision systems are used for everyday life and used as inspection equipment in production processes. Image processing technology is actively being studied. However, there is little research on the area definition for extracting objects such as character recognition or semiconductor packages. In this paper, define a region of interest and perform edge extraction to prevent the user from judging noise as an edge. We propose a noise-robust alignment correction model that can extract the edge of a region to be inspected using the distribution of edges in a specific region even if noise exists in the image. Through the proposed model, it is expected that the product production efficiency will be improved if it is applied to production field such as character recognition of tire or inspection of semiconductor packages.

키워드

OHHGBW_2018_v9n11_9_f0001.png 이미지

Fig. 1. System Configuration

OHHGBW_2018_v9n11_9_f0002.png 이미지

Fig. 2. System Process

OHHGBW_2018_v9n11_9_f0003.png 이미지

Fig. 3. Inspection Area Align Process

OHHGBW_2018_v9n11_9_f0004.png 이미지

Fig. 4. Edge Detect & Align Result

OHHGBW_2018_v9n11_9_f0005.png 이미지

Fig. 5. Edge Detect & ROI Settings

OHHGBW_2018_v9n11_9_f0006.png 이미지

Fig. 6. Applying Edge Algorithm Align Result

Table 1. Distance Value

OHHGBW_2018_v9n11_9_t0001.png 이미지

Table 2. Detection Rate

OHHGBW_2018_v9n11_9_t0002.png 이미지

참고문헌

  1. Janoczki, M., Becker, A., Jakab, L., Grof, R., & TakAcs, T. (2013). Automatic Optical Inspection of Soldering. Material Science-Advanced Topics. DOI : 10.5772/51699
  2. Seo, Y. R., Park, K., Kim, S. K., & Ra, S. W. (2011). Vibration Analysis for a Feeding Unit of Vision Inspection System of Precision Screws. Journal of The Korean Society of Manufacturing Technology Engineers, 20(4), 446-451.
  3. H. L. Song, T. W. Hur. (2011). Development of auto sorting system for T type welding nut using a vision inspector. Journal of the Institute of Electronics Engineers of Korea, 48(1), 16-24.
  4. J. J. Park, G. H. Kim, E. S. Lee. (2014). A study on the elliptical gear inspection system using machine vision. Transactions of the Korean Society of Mechanical Engineers A, 38(1), 59-63. DOI : 10.3795/KSME-A.2014.38.1.059
  5. G. S. Kim, Y. H. Park, J. S. Park, J. S. Cho. (2015). Auto parts visual inspection in severe changes in the lighting environment. Journal of Institute of Control, Robotics and Systems, 21(12), 1109-1114. DOI : 10.5302/J.ICROS.2015.15.0134
  6. G. H. Kwon, H. G. Chu, J. Y. Kim, J. H. Kang. (2017). Development of the Vision System to Inspect the Inside of the Brake Calipers. Journal of Sensor Science and Technology, 26(1), 49-43. DOI : 10.5369/JSST.2017.26.1.39
  7. G. W. Jin, (2017). A Study on the BGA Package Measurement using Noise Reduction Filters. Journal of the Korea Convergence Society, 8(11), 15-20. DOI : 10.15207/JKCS.2017.8.11.015
  8. T. H. Lee, K. R. Park, D. H. Kim. (2017). A Study on Scratch Detection of Semiconductor Package using Mask Image. Journal of the Korea Convergence Society, 8(11), 43-48. DOI : 10.15207/JKCS.2017.8.11.043
  9. J. H. Park, K. J. Lee. (2017). Realization of user-centered smart factory system using motion recognition. Journal of Convergence for Information Technology, 7(6), 153-158. DOI : 10.22156/CS4SMB.2017.7.6.153
  10. C. S. Pyo, J. Lyou. (2013). Automation of Tire Tread Extruder Line Using Cameras. Journal of Institute of Control, Robotics and Systems, 19(3), 262-267. DOI : 10.5302/J.ICROS.2013.12.179
  11. M. K. Kwon, H. S. Yang. (2017). A scene search method based on principal character identification using convolutional neural network, Journal of Convergence for Information Technology, 7(2), 31-36. DOI : 10.22156/CS4SMB.2017.7.2.031
  12. S. K. Lee, Y. S. Park, G. S. Lee, J. Y. Lee, S. H. Lee. (2013). An Automatic Object Extraction Method Using Color Features of Object and Background in Image. Journal of Digital Convergence, 11(12), 459-465. DOI : 10.14400/JDPM.2013.11.12.459
  13. M. K. Oh, J. C. Park. (2017). Long Distance Vehicle License Plate Region Detection Using Low Resolution Feature of License Plate Region in Road View Images. Journal of Digital Convergence, 15(1), 239-245. DOI : 10.14400/JDC.2017.15.1.239
  14. J. H. Lee, S. H. Byun, J. H. Nam, S. R. Cho. (2017). An Algorithm for Measurement of Pack Ice Concentration Using Localized Binarization of Quadtree-Subdivided Image. Journal of the Society of Naval Architects of Korea, 54(1), 49-56. DOI : 10.3744/SNAK.2017.54.1.49
  15. J. H. Ju, J. S. Oh. (2012). An adaptive binarization algorithm for degraded document images. The Journal of Korean Institute of Communications and Information Sciences, 37(7A), 581-585. DOI : 10.7840/KICS.2012.37.7A.581
  16. C. S. Park, H. S. Kim. (2015). FPGA Implementation for Real Time Sobel Edge Detector Block Using 3-Line Buffers. Journal of Institute of Korean Electrical and Electronics Engineers, 19(1), 10-17. DOI : 10.7471/ikeee.2015.19.1.010
  17. Zhai, X., Bensaali, F., & Ramalingam, S. (2011). Real-time license plate localisation on FPGA. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, 14-19.
  18. Xiang, H., Yan, B., Cai, Q., & Zou, G. (2011, September). An edge detection algorithm based-on Sobel operator for images captured by binocular microscope. In Electrical and Control Engineering (ICECE), 2011 International Conference on IEEE, 980-982. DOI: 10.1109/ICECENG.2011.6057846