Mean-shift-based color segmentation of images containing green vegetation

Computers and Electronics in Agriculture

Volumn 65, Issue 1, 2009, Pages 93-98

  Zheng, Liying ^a,b   Zhang, Jingtao ^b   Wang, Qianyu ^b  

^a HARBIN ENGINEERING UNIVERSITY   (China)

^b HEILONGJIANG ACADEMY OF AGRICULTURAL SCIENCES   (China)

Author keywords

Back propagation neural network; Green vegetation; Mean shift; Segmentation

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; ARTIFICIAL INTELLIGENCE; COLOR; COLOR IMAGE PROCESSING; FEATURE EXTRACTION; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE SEGMENTATION; NEURAL NETWORKS; OPTICAL PROPERTIES;

BACK PROPAGATION NEURAL NETWORK; COLOR FEATURES; COLOR IMAGES; COLOR SEGMENTATIONS; COMPLEX TASKS; FIELD CONDITIONS; GREEN VEGETATION; HSI COLOR SPACES; MEAN SHIFT; PLANT TYPES; RED , GREEN AND BLUES; RGB COLOR SPACES; SEGMENTATION; SEGMENTATION ALGORITHMS; SOIL TYPES; TWO STAGES;

VEGETATION;

ACCURACY ASSESSMENT; BACK PROPAGATION; COLOR; IMAGE CLASSIFICATION; IMAGE PROCESSING; PERFORMANCE ASSESSMENT; SOIL TYPE; VEGETATION STRUCTURE;

EID: 57349118530     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2008.08.002     Document Type: Article

References (22)

1. Aitkenhead M.J., Dalgetty I.A., Mullins C.E., McDonald A.J.S., and Strachan N.J.C. Weed and crop discrimination using image analysis and artificial intelligence methods. Computers and Electronics in Agriculture 39 3 (2003) 157-171
2. Bakker T., Wouters H., Asselt van K., Bontsema J., Tang L., Muller J., and Straten van G. A vision based row detection system for sugar beet. Computers and Electronics in Agriculture 60 1 (2008) 87-95
3. Bunting P., and Lucas R. The delineation of tree crowns in Australian mixed species forests using hyperspectral compact airborne spectrographic imager (CASI) data. Remote Sensing of Environment 101 2 (2006) 230-248
4. Cheng Y. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 8 (1995) 790-799
5. Comaniciu D., and Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 5 (2002) 603-619
6. Fukunaga K., and Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21 1 (1975) 32-40
7. Gee Ch., Bossu J., Jones G., and Truchetet F. Crop/weed discrimination in perspective agronomic images. Computers and Electronics in Agriculture 60 1 (2008) 49-59
8. Hemming J., and Rath T. PA-precision agriculture: computer-vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research 78 3 (2001) 233-243
9. Kataoka T., Kaneko T., Okamoto H., and Hata S. Crop growth estimation system using machine vision. Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2003) 1079-1083
10. Laliberte A.S., Rango A., Herrick J.E., Fredrickson Ed L., and Burkett L. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments 69 1 (2007) 1-14
11. Ling P.P., and Ruzhitsky V.N. Machine vision techniques for measuring the canopy of tomato seedling. Journal of Agricultural Engineering Research 65 2 (1996) 85-95
12. Marchant J.A., Tillett R.D., and Brivot R. Real-time segmentation of plants and weeds. Real-Time Imaging 4 4 (1998) 243-253
13. Neto J.C., Meyer G.E., and Jones D.D. Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm. Computers and Electronics in Agriculture 51 1-2 (2006) 66-85
14. Onyango C.M., and Marchant J.A. Physics-based color image segmentation for scenes containing vegetation and soil. Image and Vision Computing 19 8 (2001) 523-538
15. Onyango C.M., and Marchant J.A. Segmentation of row crop plants from weeds using color and morphology. Computers and Electronics in Agriculture 39 3 (2003) 141-155
16. Reid J., and Searcy S. Vision-based guidance of an agricultural tractor. IEEE Control Systems Magazine 7 2 (1987) 39-43
17. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. Parallel Distributed Processing, Vol. 1: Foundations (1986), MIT Press, Cambridge, MA pp. 318-362
18. Shrestha D.S., Steward B.L., and Birrell S.J. Video processing for early stage maize plant detection. Biosystems Engineering 89 2 (2004) 119-129
19. Sogaard H.T., and Olsen H.J. Determination of crop rows by image analysis without segmentation. Computers and Electronics in Agriculture 38 2 (2003) 141-158
20. Sonka M., Hlavac V., and Boyle R. Image Processing Analysis, and Machine Vision (2002), Thomson Asia Pte Led, United States of America pp. 25-26
21. Tellaeche A., Burgos-Artizzu X.P., Pajares G., and Ribeiro A. A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition 41 2 (2008) 521-530
22. Zehm A., Nobis M., and Schwabe A. Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants 198 2 (2003) 142-160