Field pest identification by an improved Gabor texture segmentation scheme

New Zealand Journal of Agricultural Research

Volumn 50, Issue 5, 2007, Pages 719-723

  Juan, Zhao ^a   Xiao Ping, Cheng ^a  

^a SOUTHWEST UNIVERSITY   (China)

Author keywords

AdaBoost; Field pests; Gabor filter; Image segmentation; SVM

Indexed keywords

ALGORITHM; AUTOMATION; CLASSIFICATION; EXTRACTION; FIELD METHOD; IDENTIFICATION METHOD; IMAGE PROCESSING; PEST SPECIES; SEGMENTATION; WAVELET;

EID: 49249125392     PISSN: 00288233     EISSN: 11758775     Source Type: Journal    
DOI: 10.1080/00288230709510343     Document Type: Article

References (18)

1. Bovik, AC, Clark, M, and Geisler, W. 1990. Multichannel texture analysis using localized spatial filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:55–73.
2. Nello, Cristianini, and John, Shawe-Taylor. 2000. An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press.
3. Daugman, JG. 1985. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A, 2(7):1160–1169.
4. Davis, LS, Johns, SA, and Aggarwal, JK. 1979. Texture analysis using generalized co-occurrence matrices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1:252–259.
5. Franz, E, Gebhardt, MR, and Unklesbay, KB. 1991. The use of local spectral properties of leaves as an aid for identification weeds seedlings in digital images. Transactions of the ASAE, 32(2):682–687.
6. Freund, Y, and Schapire, RE. 1995. “ A decision-theoretic generalization of on-line learning and an application to boosting ”. In Computational Learning Theory:Eurocolt'95, Barcelona, Spain, Germany:Springer-Verlag.
7. Freund, Y, and Schapire, RE. Experiments with a new boosting algorithm. Proceedings of the 13th International Conference on Machine Learning. San Francisco, California. pp. 148–156.
8. Freund, Y, and Schapire, R. 1999. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14(5):771–780.
9. Guyer, DE, Miles, GE, Gaulttney, LD, and Schreiber, MM. 1993. Application of machine vision to shape analysis in leaf and plant identification. Transactions of the ASAE, 36(1):163–171.
10. Meyer, GE, Mehta, T, Kocher, MF, Mortensen, DA, and Samal, A. 1998. Textural imaging and discriminate analysis for distinguishing weeds for spot spraying. Transactions of the ASAE, 41(4):1189–1197.
11. Naghdy, G, Wang, J, and Ogunbona, P. 1996. Texture analysis using Gabor wavelets. Proceedings of SPIE on Human Vision and Electronic Imaging, 2657:74–85.
12. Porat, M, and Zeevi, YY. 1989. Localized texture processing in vision:analysis and synthesis in the Gaborian space. IEEE Transactions on Biomedical Engineering, 36(1):115–129.
13. Reed, TR, Hans, Du, and Buf, JM. 1993. A review of recent texture segmentation and feature extraction techniques. Computer Vision Graphics and Image Processing:Image Understanding, 57(3):359–372.
14. Vapnik, V. 1998. Statistical learning theory, New York:John Wiley & Sons.
15. Weszka, JS, Dyer, CR, and Rosenfeld, A. 1976. A comparative study of texture measures for terrain classification. IEEE Transactions on Systems, Man, and Cybernetics, 6:269–285.
16. Woebbecke, DM, Meyer, GE, Von Bargen, K, and Mortensen, DA. 1995b. Shape features for identifying young weeds using image analysis. Transactions of the ASAE, 38(1):271–281.
17. Yonekawa, S, Sakai, N, and Kitani, O. 1996. Identification of idealized leaf types using simple dimensionless shape factors by image analysis. Transactions of the ASAE, 39(4):1525–1533.
18. Zhang, N, and Chaisattapagon, C. 1995. Effective criteria for weed identification in wheat fields using machine vision. Transactions of the ASAE, 38(3):965–974.