Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Boosting a weak learning algorithm by majority
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Boosting Chain Learning for Object Detection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SceBoost Learning Algorithm for Feature Selection
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
Identification of citrus disease using color texture features and discriminant analysis
Computers and Electronics in Agriculture
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Citrus canker, a bacterial disease of citrus tree leaves, causes significant damage to citrus production worldwide. Effective and fast disease detection methods must be undertaken to minimize the losses of citrus canker infection. In this paper, we present a new approach based on global features and zone-based local features to detect citrus canker from leaf images collected in field which is more difficult than the leaf images captured in labs. Firstly, an improved AdaBoost algorithm is used to select the most significant features of citrus lesions for the segmentation of the lesions from their background. Then a canker lesion descriptor is proposed which combines both color and local texture distribution of canker lesion zones suggested by plant phytopathologists. A two-level hierarchical detection structure is developed to identify canker lesions. Thirdly, we evaluate the proposed method and its comparison with other approaches, and the experimental results show that the proposed approach achieves similar classification accuracy as human experts.