Segmentation of Rumex obtusifolius using Gaussian Markov random fields

Machine Vision and Applications

Volumn 24, Issue 4, 2013, Pages 845-854

  Hiremath, Santosh ^a   Tolpekin, Valentyn A ^b   Van Der Heijden, Gerie ^a   Stein, Alfred ^b  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

Broad leaved dock; GMRF texture; Image analysis; Rumex obtusifolius; Segmentation; Weed control

Indexed keywords

ADVERSE ENVIRONMENTAL IMPACTS; CONTROLLED ENVIRONMENT; EXISTING SYSTEMS; GAUSSIAN MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; REAL TIME ALGORITHMS; REAL-TIME APPLICATION; RUMEX OBTUSIFOLIUS;

ALGORITHMS; HERBICIDES; IMAGE ANALYSIS; IMAGE SEGMENTATION; MARKOV PROCESSES; TEXTURES;

WEED CONTROL;

EID: 84877814507     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-012-0470-0     Document Type: Article

References (35)

1. Ahmad, U.; Kondo, N.; Arima, S.; Monta, M.; Mohri, K.: Weed detection in lawn field using machine vision: utilization of textural features in segmented area. J. JSAM 61(2), 61-69 (1999)
2. Ahmad, U.; Konda, N.; Monta, M.; Shibano, Y.; Arima, S.; Nakamura, H.: Feasibility of weed detection in lawn field based on gray-scale uniformity. In: JSAM Annaul Meeting on Agricultural, Machinery (1997)
3. Aitkenhead, M.J.; Dalgetty, I.A.; Mullins, C.E.; McDonald, A.J.S.; Strachan, N.J.C.: Weed and crop discrimination using image analysis and artificial intelligence methods. Comput. Electron. Agric. 39(3), 157-171 (2003)
4. Besag, J.: On the statistical analysis of dirty pictures. J R Stat Soc Ser B (Method) 48(3), 259-302 (1986)
5. Blake, A.; Rother, C.; Brown, M.; Perez, P.; Torr, P.: Interactive image segmentation using an adaptive gmmrf model. In: Pajdla, T.; Matas, J. (eds.) Computer Vision - Eccv 2004, Pt 1. Lecture Notes In Computer Science, vol. 3021, pp. 428-441 (2004)
6. Bond, W.; Grundy, A.: Non-chemical weed management in organic farming systems. Weed Res. 41(5), 383-405 (2001)
7. Boykov, Y.; Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: Proceedings of 9th IEEE International Conference on Computer Vision, vol. 1, pp. 26-33. Nice, France (2003)
8. Boykov, Y.; Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124-1137 (2004)
9. Boykov, Y.; Veksler, O.; Zabih, R.: Markov random fields with efficient approximations. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 648-655. Santa Barbara, CA, USA. doi: 10.1109/CVPR.1998.698673 (1998)
10. Boykov, Y.; Veksler, O.; Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222-1239 (2001)
11. Chellappa, R.: Two-dimensional discrete gaussian markov random field models for image processing. Prog. Pattern Recognit. 2, 79-112 (1985)
12. Chellappa, R.; Chatterjee, S.: Classification of textures using Gaussian Markov random fields. IEEE Trans. Acoust. Speech Signal Process. Assp 33(4), 959-963 (1985)
13. Clausi, D.A.: Comparison and fusion of co-occurrence, gabor and mrf texture features for classification of sar sea-ice imagery. Atmos. Ocean 39(3), 183-194 (2001)
14. Duda, R.; Hart, P.; Stork, D.; et al.: Pattern Classification, vol. 2. Wiley, New York (2001)
15. Efros, A.; Leung, T.: Texture synthesis by non-parametric sampling. In: International Conference on Computer Vision, pp. 1033-1038 (1999)
16. Geman, S.; Geman, D.: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721-741 (1984)
17. Guijarro, M.; Pajares, G.; Riomoros, I.; Herrera, P.J.; Burgos-Artizzu, X.P.; Ribeiro, A.: Automatic segmentation of relevant textures in agricultural images. Comput. Electron. Agric. 75(1), 75-83 (2011)
18. Guyon, I.; Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(3), 1157-1182 (2003)
19. Haralick, R.M.; Shanmugam, K.; Dinstein, I.: Textural features for image classification. Syst. Man Cybern. IEEE Trans. 3(6), 610-621 (1973)
20. Juan, O.; Boykov, Y.: Active graph cuts. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, vol. 1, pp. 1023-1029. New York, NY, USA. doi: 10.1109/CVPR.2006.47 (2006)
21. Kammler, D.: A First Course in Fourier Analysis. Cambridge University Press, Cambridge (2007)
22. Kempenaar, C.; Lotz, L.; Van Der Horst, C.; Beltman, W.; Leemans, K.; Bannink, A.: Trade off between costs and environmental effects of weed control on pavements. Crop Prot. 26(3), 430-435 (2007)
23. Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671-680 (1983)
24. Kohavi, R.; John, G.: Wrappers for feature subset selection. Artif. Intell. 97(1-2), 273-324 (1997)
25. Lafferty, J.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data, pp. 282-289. Morgan Kaufmann, Los Altos, CA (2001)
26. Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer New York, Inc.; Secaucus, NJ, USA (2001)
27. Otsuka, A.; Taniwaki, K.: Extraction of broad-leaf weeds from grassland by texture analysis. J. Jpn. Soc. Photogramm. Remote Sens. 38(6), 6-13 (1999)
28. Rother, C.; Kolmogorov, V.; Blake, A.: Grabcut - interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309-314 (2004)
29. Slaughter, D.; Giles, D.; Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61(1), 63-78 (2008)
30. Szeliski, R.; Zabih, R.; Scharstein, D.; Veksler, O.; Kolmogorov, V.; Agarwala, A.; Tappen, M.; Rother, C.: A comparative study of energy minimization methods for Markov Random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 1068-1080 (2008)
31. Van Evert, F.; Polder, G.; Van Der Heijden, G.; Kempenaar, C.; Lotz, L.: Real-time vision-based detection of rumex obtusifolius in grassland. Weed Res. 49(2), 164-174 (2009)
32. Vogiatzis, G.; Torr, P.; Cipolla, R.: Multi-view stereo via volumetric graph-cuts. In: Schmid, C.; Soatto, S.; Tomasi, C. (eds.) In: 2005 IEEE Computer Society Conference On Computer Vision And Pattern Recognition, vol. 2, pp. 391-398 (2005)
33. Watchareeruetai, U.; Takeuchi, Y.; Matsumoto, T.; Kudo, H.; Ohnishi, N.: Modified lawn weed detection: utilization of edge-color based SVM and grass-model based blob inspection filterbank. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4985, LNCS (2008)
34. Zalesny, A.; Gool, L.V.: A compact model for viewpoint dependent texture synthesis. Lecture Notes in Computer Science, pp. 124-143. Springer, Berlin (2001)
35. Zhao, Y.; Zhang, L.; Li, P.; Huang, B.: Classification of high spatial resolution imagery using improved gaussian markov random-field-based texture features. Geosci. Remote Sens. IEEE Trans. 45(5), 1458-1468 (2007)