Abstract
A partial differential equation based diffusion is presented for crop rows detection. In the diffusion, the evolving direction is estimated through the vanishing point, which is one of global feature of row-crop images. According to the vanishing point, we generate the orientations of row crop textures, and then integrate the induced field of directions into an oriented diffusion. After processing the row-crop image with the new diffusion, we extract the crop rows from its black-white version using a morphological operation. Experiments on the real row-crop image data show the proposed diffusion can suppress the undesired interference more efficiently than the other diffusion when extracting crop rows.
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References
Leemans, V., Destain, M.-F.: Line cluster detection using a variant of the Hough transform for culture row localisation. Image Vis. Comput. 24(5), 541–550 (2006)
Roviramas, F., Zhang, Q., Reid, J.F., Will, J.D.: Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 219(8), 999–1010 (2005)
Perezortiz, M., Pena, J.M., Gutierrez, P.A., Torressanchez, J., Hervasmartinez, C., Lopezgranados, F.: A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Appl. Soft Comput. 37, 533–544 (2015)
Ji, R., Qi, L.: Crop-row detection algorithm based on random Hough transformation. Math. Comput. Model. 54(3), 1016–1020 (2011)
Romeo, J., Pajares, G., Montalvo, M., Guerrero, J.M., Guijarro, M., Ribeiro, A.: Crop row detection in maize fields inspired on the human visual perception. Sci. World J. 2012, Article ID 484390 (2012)
Reiser, D.: Crop row detection in maize for developing navigation algorithms under changing plant growth stages. In: Robot 2015: Second Iberian Robotics Conference, pp. 371–382 (2016)
Guerrero, J.M., Guijarro, M., Montalvo, M., Romeo, J., Emmi, L., Ribeiro, A., Pajares, G.: Automatic expert system based on images for accuracy crop row detection in maize fields. Expert Syst. Appl. 40(2), 656–664 (2013)
Vidović, I., Cupec, R., Hocenski, Z.: Crop row detection by global energy minimization. Pattern Recognit. 55, 68–86 (2016)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
You, Y.-L., Kaveh, M.: Fourth-order partial differential equations for noise removal. IEEE Trans. Image Process. 9(10), 1723–1730 (2000)
Alvarez, L., Lions, P.-L., Morel, J.-M.: Image selective smoothing and edge detection by nonlinear diffusion. II. SIAM J. Numer. Anal. 29(3), 845–866 (1992)
Weickert, J.: Coherence-enhancing diffusion filtering. Int. J. Comput. Vis. 31(2–3), 111–127 (1999)
Tschumperle, D., Deriche, R.: Vector-valued image regularization with PDEs: a common framework for different applications. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 506–517 (2005)
Weickert, J.: Anisotropic Diffusion in Image Processing, vol. 16, p. 272. Teubner, Stuttgart (1996)
Sapiro, G.: Geometric Partial Differential Equations and Image Analysis. Cambridge University Press, Cambridge (2006)
Witkin, A.: Scale-space filtering: a new approach to multi-scale description. In: Acoustics, Speech, and Signal Processing, IEEE ICASSP 1984, vol. 9, pp. 150–153 (1984)
Chan, T.F., Shen, J.J.: Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods, pp. 110–115. Society for Industrial and Applied Mathematics, Philadelphia (2005)
Guidotti, P., Lambers, J.V.: Two new nonlinear nonlocal diffusions for noise reduction. J. Math. Imag. Vis. 33(1), 25–37 (2009)
Guidotti, P., Longo, K.: Two enhanced fourth order diffusion models for image denoising. J. Math. Imag. Vis. 40(2), 188–198 (2011)
Chen, Y., Barcelos, C.A.Z., Mair, B.A.: Smoothing and edge detection by time-varying coupled nonlinear diffusion equations. Comput. Vis. Image Underst. 82(2), 85–100 (2001)
Luo, H., Zhu, L., Ding, H.: Coupled anisotropic diffusion for image selective smoothing. Signal Process. 86(7), 1728–1736 (2006)
Tang, C., Han, L., Ren, H., Gao, T., Wang, Z., Tang, K.: The oriented-couple partial differential equations for filtering in wrapped phase patterns. Opt. Express 17(7), 5606–5617 (2009)
Heydari, M., Karami, M., Babakhani, A.: A new adaptive coupled diffusion PDE for MRI Rician noise. SIViP 10(7), 1211–1218 (2016)
Xu, J., Jia, Y., Shi, Z., Pang, K.: An improved anisotropic diffusion filter with semi-adaptive threshold for edge preservation. Signal Process. 119(C), 80–91 (2016)
Ramosllordn, G., Vegassnchezferrero, G., Martinfernandez, M., Alberolalpez, C., Ajafernndez, S.: Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans. Image Process. 24(1), 345–358 (2015)
Jain, S.K., Ray, R.K.: An alternative framework of anisotropic diffusion for image denoising. In: International Conference on Information and Communication Technology for Competitive Strategies, pp. 1–6 (2016)
Yuan, J.: Improved anisotropic diffusion equation based on new non-local information scheme for image denoising. IET Comput. Vis. 9(6), 864–870 (2015)
Cho, S.I., Kang, S.-J., Kim, H.-S., Kim, Y.H.: Dictionary-based anisotropic diffusion for noise reduction. Pattern Recognit. Lett. 46, 36–45 (2014)
Tsiotsios, C., Petrou, M.: On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recognit. 46(5), 1369–1381 (2013)
Yu, X., Wu, C., Jia, T., Chen, S.: A time-dependent anisotropic diffusion image smoothing method. In: International Conference on Intelligent Control and Information Processing, pp. 859–862 (2011)
Tang, C., Wang, Z., Wang, L., Wu, J., Gao, T., Yan, S.: Estimation of fringe orientation for optical fringe patterns with poor quality based on Fourier transform. Appl. Opt. 49(4), 554–561 (2010)
Wang, H., Qian, K., Gao, W., Lin, F., Seah, H.S.: Fringe pattern denoising using coherence-enhancing diffusion. Opt. Lett. 34(8), 1141–1143 (2009)
Tang, C., Han, L., Ren, H., Zhou, D., Chang, Y., Wang, X., Cui, X.: Second-order oriented partial-differential equations for denoising in electronic-speckle-pattern interferometry fringes. Opt. Lett. 33(19), 2179–2181 (2008)
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 777–789 (1998)
Tellaeche, A., BurgosArtizzu, X.P., Pajares, G., Ribeiro, A., Fernandez-Quintanilla, C.: A new vision-based approach to differential spraying in precision agriculture. Comput. Electron. Agric. 60(2), 144–155 (2008)
Hughes, C., McFeely, R., Denny, P., Glavin, M., Jones, E.: Equidistant (fθ) fish-eye perspective with application in distortion centre estimation. Image Vis. Comput. 28(3), 538–551 (2010)
Kong, H., Audibert, J.-Y., Ponce, J.: Vanishing point detection for road detection. In: Computer Vision and Pattern Recognition, pp. 96–103 (2009)
Tsai, Y.-M., Chang, Y.-L., Chen, L.-G.: Block-based vanishing line and vanishing point detection for 3D scene reconstruction. In: 2006 International Symposium on Intelligent Signal Processing and Communications, pp. 586–589 (2006)
Barnard, S.T.: Interpreting perspective images. Artif. Intell. 21(4), 435–462 (1983)
McLean, G., Kotturi, D.: Vanishing point detection by line clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(11), 1090–1095 (1995)
Förstner, W.: Optimal vanishing point detection and rotation estimation of single images from a legoland scene. In: Proceedings of ISPRS Commission III Symposium on Photogrammetric Computer Vision and Image Analysis, pp. 157–162 (2010)
Almansa, A., Desolneux, A., Vamech, S.: Vanishing point detection without any a priori information. IEEE Trans. Pattern Anal. Mach. Intell. 25(4), 502–507 (2003)
Antolovic, D., Leykin, A., Johnson, S.D.: Vanishing point: a visual road-detection program for a DARPA grand challenge vehicle. Indiana University (2005)
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This work is supported by the National Natural Science Foundation of China (NNSFC) (Grant 61561025 and 71561014).
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Wu, J., Deng, M., Fu, L., Miao, J. (2019). Vanishing Point Conducted Diffusion for Crop Rows Detection. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_54
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DOI: https://doi.org/10.1007/978-3-030-02804-6_54
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