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Most of today’s chromatic adaptation transforms (CATs) are based on a modified form of the von Kries chromatic adaptation model, which states that chromatic adaptation is an independent gain regulation of the three photoreceptors in the human visual system ...
This paper presents a novel scheme for fast color invariant ball detection in the RoboCup context. Edge filtered camera images serve as an input for an Ada Boost learning procedure that constructs a cascade of classification and regression trees (CARTs). Our ...
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binar ...
Department of Statistics, University of Berkeley, CA2004
We compare the consensus and uniform consensus problems in synchronous systems. In contrast to consensus, uniform consensus is not solvable with byzantine failures. This still holds for the omission failure model if a majority of processes may be faulty. F ...
This report presents a robust syntactic parser that is able to return a "correct" derivation tree even if the grammar cannot generate the input sentence. The following two step solution is prop osed: the finest corresponding most probable optimal maximum c ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
Although the subject of fusion is well studied, the effects of normalisation prior to fusion are somewhat less well investigated. In this study, four normalisation techniques and six commonly used fusion classifiers were examined. Based on 24 (fusion class ...
In earlier work~\cite{Lepetit04b}, we proposed to treat wide baseline matching of feature points as a classification problem and proposed an implementation based on K-means and nearest neighbor classification. We showed that this method is both reliable an ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
Set agreement, where processors decisions constitute a set of outputs, is notoriously harder to analyze than consensus where the decisions are restricted to a single output. This is because the topological questions that underly set agreement are not about ...
