@conference {13625,
	title = {Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {252 - 255 Vol.1 - 252 - 255 Vol.1},
	abstract = {In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.},
	keywords = {approximation;, Bayes, Bayesian, bound, character, classification;, classifiers;, conditional, distribution;, entropy;, error, formalism;, handwritten, methods;, minimisation;, multiple, numerals;, pattern, probability, probability;, Product, rate;, recognition;, statistics;, unconstrained, upper},
	doi = {10.1109/ICPR.2004.1334071},
	author = {Kang,Hee-Joong and David Doermann}
}
@article {18193,
	title = {Data hiding in image and video .I. Fundamental issues and solutions},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/06//},
	pages = {685 - 695},
	abstract = {We address a number of fundamental issues of data hiding in image and video and propose general solutions to them. We begin with a review of two major types of embedding, based on which we propose a new multilevel embedding framework to allow the amount of extractable data to be adaptive according to the actual noise condition. We then study the issues of hiding multiple bits through a comparison of various modulation and multiplexing techniques. Finally, the nonstationary nature of visual signals leads to highly uneven distribution of embedding capacity and causes difficulty in data hiding. We propose an adaptive solution switching between using constant embedding rate with shuffling and using variable embedding rate with embedded control bits. We verify the effectiveness of our proposed solutions through analysis and simulation.},
	keywords = {adaptive, analysis;, bits;, colour, condition;, constant, CONTROL, data, embedded, EMBEDDING, embedding;, encapsulation;, extractable, hiding;, image, Modulation, modulation;, multilevel, multiplexing, multiplexing;, NOISE, nonstationary, processing;, rate;, reviews;, shuffling;, signal, signals;, simulation;, solution;, techniques;, variable, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.810588},
	author = {M. Wu and Liu,Bede}
}
@conference {13246,
	title = {Modelling pedestrian shapes for outlier detection: a neural net based approach},
	booktitle = {Intelligent Vehicles Symposium, 2003. Proceedings. IEEE},
	year = {2003},
	month = {2003/06//},
	pages = {428 - 433},
	abstract = {In this paper we present an example-based approach to learn a given class of complex shapes, and recognize instances of that shape with outliers. The system consists of a two-layer custom-designed neural network. We apply this approach to the recognition of pedestrians carrying objects from a single camera. The system is able to capture and model an ample range of pedestrian shapes at varying poses and camera orientations, and achieves a 90\% correct recognition rate.},
	keywords = {(artificial, complex, Computer, computing;, custom, design;, detection;, engineering, intelligence);, layer, learning, method;, modelling;, net;, nets;, neural, object, outlier, pedestrian, pedestrians, rate;, recognition, recognition;, SHAPE, shapes;, traffic, two, vision;},
	doi = {10.1109/IVS.2003.1212949},
	author = {Nanda,H. and Benabdelkedar,C. and Davis, Larry S.}
}
