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Hardware Implementation of FAST Algorithm for Mobile Applications

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Abstract

Simple inexpensive cameras are often built in small devices such as mobile phones or mp3 players. Besides the usual image recording, other ways of their use have been proposed which usually involve intensive image processing. In such processing, corner detection is often found as a preliminary operation. Many corner detection algorithms have been introduced, but due to their computational complexity very few are suitable for real-time applications. One of novel approaches to corner detection is the so called FAST algorithm which is specially optimized for speed. However, on simple and slow devices even this algorithm can be too slow and energy consuming when executed on the in-built processor. In this paper we present hardware implementation of FAST algorithm, capable of processing images at constant speed of one pixel per clock. The results showed that nearly forty times faster corner detection could be achieved on mobile object detection and localization application, if the existing software detector is replaced by our hardware module.

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  1. Source code and precompiled binaries can be found on author’s homepage.

References

  1. Benedetti, A., Prati, A., Scarabottolo, N. (1998). Image convolution on fpgas: the implementation of a multi-fpga fifo structure. In Euromicro conference, 1998. Proceedings 24th (Vol. 1, pp. 123–1300). doi:10.1109/EURMIC.1998.711786.

  2. Canny, J. (1986). A computational approach to edge detection. Pattern analysis and machine intelligence. IEEE Transactions on PAMI, 8(6), 679–698. doi:10.1109/TPAMI.1986.4767851.

    Article  Google Scholar 

  3. Dohi, K., Yorita, Y., Shibata, Y., Oguri, K. (2011). Pattern compression of fast corner detection for efficient hardware implementation. In Field programmable logic and applications (FPL), 2011 international conference on (pp. 478–481). doi:10.1109/FPL.2011.94.

  4. Huang, W., Gao, Y., Chan, K.L. (2010). A review of region-based image retrieval. Journal of Signal Processing Systems, 59(2), 143–161. doi:10.1007/s11265-008-0294-3.

    Article  Google Scholar 

  5. Lu, C.L., & Fu, L.C. (2007). Hardware architecture to realize multi-layer image processing in real-time. In Industrial electronics society, 2007. IECON 2007. 33rd annual conference of the IEEE (pp. 2478–2483). doi:10.1109/IECON.2007.4460387.

  6. Omercevic, D., & Leonardis, A. (2009). Hyperlinking reality via camera phones. In CHI ’09 extended abstracts on human factors in computing systems, CHI EA ’09 (pp. 3515–3516). New York: ACM. doi:10.1145/1520340.1520519.

    Google Scholar 

  7. Perri, S., Lanuzza, M., Corsonello, P., Cocorullo, G. (2003). Simd 2-d convolver for fast fpga-based image and video processors. In 2003 MAPLD technical program. September 9-11. Washington.

  8. Rosten, E., & Drummond, T. (2005). Fusing points and lines for high performance tracking. In International conference on computer vision (pp. 1508–1515): Springer.

  9. Rosten, E., & Drummond, T. (2006). Machine learning for high-speed corner detection In European conference on computer vision (pp. 430–443).

  10. Rosten, E., Porter, R., Drummond, T. (2010). Faster and better: a machine learning approach to corner detection. IEEE Transactions Pattern Analysis and Machine Intelligence, 32, 105–119. doi:10.1109/TPAMI.2008.275. arXiv:0810.2434.

    Article  Google Scholar 

  11. Taylor, S., Rosten, E., Drummond, T. (2009). Robust feature matching in 2.3s. In IEEE CVPR workshop on feature detectors and descriptors: the state of the art and beyond.

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Correspondence to Domen Šoberl.

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Šoberl, D., Zimic, N., Leonardis, A. et al. Hardware Implementation of FAST Algorithm for Mobile Applications. J Sign Process Syst 79, 247–256 (2015). https://doi.org/10.1007/s11265-013-0843-2

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  • DOI: https://doi.org/10.1007/s11265-013-0843-2

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