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A Study of Edge Detection for Auto Focus of Infrared Camera

  • Park, Hee-Duk (Electro-Optics 2 Team, Hanwha Systems Co., Ltd.)
  • Received : 2017.12.09
  • Accepted : 2017.12.23
  • Published : 2018.01.31

Abstract

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

Keywords

References

  1. Ibarra-Castanedo C. "Quantitative subsurface defect evaluation by pulsed phase thermography: depth retrieval with the phase," Ph. D. thesis, Laval University, p.128, Oct. 2005.
  2. J. Shin, S. Ye, B. Kim and C. Park, "Image Correction Method for Uncooled IR TECless Detector with Non-linear characteristics due to Temperature Change," Journal of The Korea Society of Computer and Information, Vol. 22, No. 10, pp.19-26, Dec. 2017.
  3. Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing," Prentice Hall, pp.189-221, 2009.
  4. S. Kim, S. Nam and H. Lim, "An Improved Area Edge Detection for Real-time Image Processing," Journal of The Korea Society of Computer and Information, Vol. 14, No. 1, pp.99-106, Jan. 2009.
  5. Raman Maini and Dr. Himanshu Aggarwal, "Study and Comparison of Various Image Edge Detection Techniques," International Journal of Image Processing (IJIP), vol. 3, issue 1, pp.1-12, Feb. 2009.
  6. J. Canny, "A computation approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no 6, pp. 769-798, Nov. 1986.
  7. H. Shan and N. A. Hazanchuk, "Adaptive Edge Detection for Real-Time Video Processing using FPGAs," Application notes, Altera Corporation, 2005.
  8. Heeduk Park, "FPGA Design and Implementation of Edge Enhancement by Using 3x3 Mask Filter," International Conference Industry Technology, pp. 630-635, Jan. 2014.
  9. A. Benedetti, A. Prati, and N. Scarabottolo, "Image convolution on FPGAs: The implementation of a multi-FPGA FIFO structure," in Proc. Euromicro Conf., pp. 123-130, 1998.
  10. Z. Guo, W. Xu, and Z. Chai, "Image edge detection based on fpga," 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, pp.169-171, 2010.
  11. K. Moon, "Spatial Compare Filter Based Real-Time Dead Pixel Correction Method for Infrared Camera," Journal of The Korea Society of Computer and Information, Vol. 21, No. 12, pp.35-41, Dec. 2016. https://doi.org/10.9708/jksci.2016.21.12.035