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We present in this work an unsupervised image classifier, which is capable of clustering images taken by an unknown number of unknown digital cameras into a number of classes, each corresponding to one camera. The classification system first extracts and enhances a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. Secondly, it applies...
We present the first report on Granger causality based detection of functional modules from temporal gene expression data. The approach uses temporal causal relationships shared between pair of genes to derive a connection matrix, which is further analyzed using graph-theoretic techniques. The approach is evaluated against a synthesized dataset and a real biological dataset obtained for Arabidopsis...
In this paper we demonstrate the inherent robustness of minimum distance estimator that makes it a potentially powerful tool for parameter estimation in gene expression time course analysis. To apply minimum distance estimator to gene expression clustering, a partial mixture model that can naturally incorporate replicate information and allow scattered genes is formulated specially for tight clustering...
A key challenge of gene expression time series research is the development of efficient and reliable probabilistic models. In response, we propose an unsupervised conditional random fields (CRFs) model for gene expression time series clustering. Conditional random fields have demonstrated superior performance over generative models such as hidden Markov models (HMMs) in terms of computational efficiency...
In this work, we propose a steganographic scheme based on an expandable progressive exponential clustering (EPEC) algorithm for embedding secret message in colour images, which aims to achieve high embedding capacity while keeping distortion low. The EPEC algorithm partitions the colour table into clusters of size equal to a power of 2 so that embeddable pixels can be used efficiently. Our experiments...
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