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Applying Image Processing Technology to Region Area Estimation

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Genetic and Evolutionary Computing (ICGEC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 579))

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Abstract

This paper proposes a method to measure a region area of field by using aerial images. An unmanned aerial vehicle (UAV) and image processing technology is used to capture images of the land and measure its area. The main advantage of using UAV to capture images is the higher degree of freedom; it can accord user’s operation to capture from various angles and heights to obtain more diversified information. Even taking pictures of a dangerous area, the user can remote the UAV in a safer place, and get the information of the area or the UAV in real time. In the experiment, an UAV is used to get images of the playground grassland which region area is known, and capture a group of images with same area from 70 to 120 m height every ten meters. In image processing process, edge detection and morphology are used to find the range of the interest region, and then count the number of pixels of it. We can get the relation between the different height and per pixels of the real area. Experimental results show that the average deviations of estimating unknown area are less than 2%.

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References

  1. Rahman, M.A., Purnama, I.K.E., Purnomo, M.H.: Simple method of human skin detection using HSV and YCbCr color spaces. In: 2014 IEEE International Conference on Intelligent Autonomous Agents, Networks and Systems (INAGENTSYS), pp. 58–61 (2014)

    Google Scholar 

  2. Liu, F., Liu, X., Chen, Y.: An efficient detection method for rare colored capsule based on RGB and HSV color space. In: 2014 IEEE International Conference on Granular Computing (GrC), pp. 175–178 (2014)

    Google Scholar 

  3. Wang, W., He, Y., Li, Z., Chen, Z.: A real-time target detection algorithm for Infrared Search and track system based on ROI extraction. In: 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), pp. 774–778 (2012)

    Google Scholar 

  4. Yitzhaky, Y., Peli, E.: A method for objective edge detection evaluation and detector parameter selection. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 1027–1033 (2003)

    Article  Google Scholar 

  5. Qiu, T., Yan, Y., Gang, L.: An auto-adaptive edge-detection algorithm for flame and fire image processing. IEEE Trans. Instrum. Meas. 61(5), 1486–1493 (2012)

    Article  Google Scholar 

  6. Peng, B., Zhang, L., Zhang, D.: Automatic image segmentation by dynamic region merging. IEEE Trans. Image Process. 20(12), 3592–3605 (2011)

    Article  MathSciNet  Google Scholar 

  7. Vladimir, T., Jeon, D., Kim, D.-H., Chang, C.-H., Kim, J.: Experimental feasibility analysis of ROI-based hough transform for real-time line tracking in auto-landing of UAV. In: 2012 15th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW), pp. 130–135 (2012)

    Google Scholar 

  8. Bi, J., Mao, W., Gong, Y.: Research on image mosaic method of UAV image of earthquake emergency. In: Third International Conference on Agro-geoinformatics, Agro-geoinformatics 2014, pp. 1–6 (2014)

    Google Scholar 

  9. Larson, R., Falvo, D.C.: Elementary Linear Algebra, 6th edn. Brook/Cole Cengage Learning, Boston (2010)

    Google Scholar 

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Acknowledgments

This work was supported by the Ministry of Science and Technology under Grant MOST 103-2221-E-018-017- and MOST 105-2221-E-018-023-.

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Correspondence to Chao-Hsing Hsu .

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Chung, YN., Hu, YJ., Tsai, XZ., Hsu, CH., Lai, CW. (2018). Applying Image Processing Technology to Region Area Estimation. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_10

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  • DOI: https://doi.org/10.1007/978-981-10-6487-6_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6486-9

  • Online ISBN: 978-981-10-6487-6

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