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제곱근 연산 횟수 감소를 이용한 Canny Edge 검출에서의 전력 소모개선

Improvement of Power Consumption of Canny Edge Detection Using Reduction in Number of Calculations at Square Root

  • Hong, Seokhee (Department of Electronics, Myongji University) ;
  • Lee, Juseong (Department of Electronics,Osan University) ;
  • An, Ho-Myoung (Department of Electronics,Osan University) ;
  • Koo, Jihun (Department of Smart IT, Osan University) ;
  • Kim, Byuncheul (Department of Electronic Engineering, Gyeongnam National University of Science and Technology)
  • 투고 : 2020.11.08
  • 심사 : 2020.11.22
  • 발행 : 2020.12.30

초록

본 논문에서는 영상처리에 사용되는 Canny edge 검출 알고리즘 중 가장 높은 연산 복잡도를 가진 제곱근 연산 횟수를 감소시키는 방법을 제안한다. 제안하는 방법은 기울기 벡터 연산 과정에 사용되는 제곱근 연산을 이용할 때 일부 픽셀에 특정한 규칙을 사용해 홀을 만들어 제곱근 연산을 직접 하지 않고 주변 픽셀들의 연속성을 이용하여 기울기 벡터를 계산하여 연산 횟수를 감소시킨다. 다양한 테스트 이미지를 이용해 실험한 결과 홀이 1개인 경우 약 97%, 홀을 증가시키면 각각 약 94%, 90%, 88%의 일치율을 보였고, 홀이 1개인 경우에는 0.2ms의 연산시간이 감소되었고, 홀을 증가시키면 각각 약 0.398ms 0.6ms, 0.8ms의 연산시간이 감소되었다. 이를 바탕으로 hole이 2개인 경우 높은 정확도와 연산 수 절감을 통해 저전력 임베디드 비전 시스템을 구현할 수 있을 것으로 기대한다.

In this paper, we propose a method to reduce the square root computation having high computation complexity in Canny edge detection algorithm using image processing. The proposed method is to reduce the number of operation calculating gradient magnitude using pixel's continuity using make a specific pattern instead of square root computation in gradient magnitude calculating operation. Using various test images and changing number of hole pixels, we can check for calculate match rate about 97% for one hole, and 94%, 90%, 88% when the number of hole is increased and measure decreasing computation time about 0.2ms for one hole, and 0.398ms, 0.6ms, 0.8ms when the number of hole is increased. Through this method, we expect to implement low power embedded vision system through high accuracy and a reduced operation number using two-hole pixels.

키워드

참고문헌

  1. W. Kim, J. Lee, H. An, and J. Kim, "High-Perf ormance and Low-Complexity image Pre-Processing Method Based on Gradient-Vector Characteristics and Hardware-Block Sharing", Trans. Electr. Electron. Mater, (TEEM), vol. 18, no. 6, pp. 320-322, Dec.2017. https://doi.org/10.4313/TEEM.2017.18.6.320
  2. W. Kim, J. Lee, and H. An, "Gradient Magnitude Hardware Architecture based on Hardware Folding Design Method for Low Power image Feature Extraction Hardware Design", Journal of Korea institute of in-formation, electronics, and communication technology (KIIECT), vol. 10, no. 2, pp. 141-146, Apr. 2017. https://doi.org/10.17661/jkiiect.2017.10.2.141
  3. W. Kim, J. Lee, H. An, and B. Kim, "image Filter Optimization Method based on common sub-expression elimination for Low Power image Feature Extraction Hardware Design", Journal of Korea institute of information, electronics, and communication technology (KIIECT), vol. 10, no. 2, pp. 192-197, Apr. 2017. https://doi.org/10.17661/jkiiect.2017.10.2.192
  4. W. Kim, J. Lee, and H. An, "Low Complexity Gradient Magnitude Calculator Hardware Architecture Using Characteristic Analysis of Projection Vector and Hardware Resource Sharing", Journal of Korea institute of information, electronics, and communication technology (KIIECT), vol. 9, no. 4, pp. 414-418, Aug. 2016. https://doi.org/10.17661/jkiiect.2016.9.4.414
  5. J. Lee, H. An, and B. Kim, "Low Complexity image Thresholding Based on Block Type Classification for Implementation of the Low Power Feature Extraction Algorithm", Journal of Korea institute of information, electronics, and communication technology (KIIECT), vol. 10, no. 2, pp. 141-146, Apr. 2017. https://doi.org/10.17661/jkiiect.2017.10.2.141
  6. J. Lee and H. An, "A Study on Implementation of the High Speed Feature Extraction System Based on Block Type Classification", Journal of Korea institute of information, electronics, and communication technology (KIIECT), vol. 12, no. 3, pp. 186-191, Jun. 2019. https://doi.org/10.17661/JKIIECT.2019.12.3.186
  7. J. Lee, H. An, and J. Kim, "Implementation of the High-Speed Feature Extraction Algorithm Based on Energy Efficient Threshold Value Selection", Trans. Electr. Electron. Mater, (TEEM), vol. 21, pp. 150-156, Feb. 2020. https://doi.org/10.1007/s42341-020-00188-x
  8. J. Canny, "A Computational Approach to Edge Detection", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, Nov. 1986. https://doi.org/10.1109/TPAMI.1986.4767851
  9. J. Lee, H. Tang, and J Park, "Energy efficient canny edge detector for advanced mobile vision applications" IEEE Trans. on Circuits and Systems for Video Technology, vol. 28, no. 4, pp. 1037-1046, Apr. 2018. https://doi.org/10.1109/tcsvt.2016.2640038