RGB to HSV Conversion Based on FPGA

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Frontier Computing (FC 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 747))

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Abstract

By analyzing the conversion algorithm between RGB and HSV, this paper proposes a fast algorithm to convert RGB space to HSV space. The algorithm adopts shift operation and lookup table instead of floating-point multiplication, which greatly improves the speed of the algorithm on FPGA. In addition, the Y component is no longer involved in the calculation during the conversion, which further reduces the computational complexity. Finally, experiments show that compared with the traditional algorithm, the algorithm can save 80% of the computing time on the FPGA platform and 46% on the PC platform. Therefore, this algorithm is widely used in real-time video analysis such as license plate recognition and flame detection.

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References

  1. Chen, J., S. Yi, Y. Qin, and X. Wang. 2016. Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai-Tibetan Plateau. International Journal of Remote Sensors 37: 1922–1936.

    Google Scholar 

  2. Chianucci, F., L. Disperati, D. Guzzi, D. Bianchini, V. Nardino, C. Lastri, and A. Rindinella. 2016. Corona estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV. The International Journal of Applied Earth Observation and Geoinformation 47: 60–68.

    Google Scholar 

  3. Chen, J., L. Ma, X. Chen, and Y. Rao. 2016. Research progress of spectral mixture analysis. Journal of Remote Sensors 20: 1102–1109.

    Google Scholar 

  4. de la Casa, G., L. Ovando, J. Bressanini, G. Martínez, and C. Díaz. 2018. Miranda Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot ISPRS. Journal of Photogrammetry and Remote Sensing 146: 531–547.

    Google Scholar 

  5. Hu, P., W. Guo, S.C. Chapman, Y. Guo, and B. Zheng. 2019. Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding. ISPRS Journal of Photogrammetry and Remote Sensing 154: 1–9.

    Google Scholar 

  6. **, X., S. Liu, F. Baret, M. Hemerlé, and A. Comar. 2017. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment 198: 105–114.

    Google Scholar 

  7. Li, L., X. Mu, C. Macfarlane, W. Song, J. Chen, K. Yan, G. Yan. 2018. A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images. Agricultural and Forest Meteorology 262: 379–390.

    Google Scholar 

  8. Li, L., G. Yan, X. Mu, Liu Suhong, Y. Chen, K. Yan, J. Luo, and W. Song. 2017. Estimation of fractional vegetation cover using mean-based spectral unmixing method. In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3178–3180. IEEE.

    Google Scholar 

  9. Rasmussen, J., G. Ntakos, J. Nielsen, J. Svensgaard, R.N. Poulsen, and S. Christensen. 2016. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots. European Journal of Agrononomy 74: 75–92.

    Google Scholar 

  10. L. Roth, H. Aasen, A. Walter, F. Liebisch. 2018. Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography. ISPRS Journal of Photogrammetry and Remote Sensing 141: 161–175.

    Google Scholar 

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Correspondence to Xv Li .

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Qi, F., Li, X., Zhang, G. (2021). RGB to HSV Conversion Based on FPGA. In: Chang, JW., Yen, N., Hung, J.C. (eds) Frontier Computing. FC 2020. Lecture Notes in Electrical Engineering, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-16-0115-6_79

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  • DOI: https://doi.org/10.1007/978-981-16-0115-6_79

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0114-9

  • Online ISBN: 978-981-16-0115-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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