Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Information-hiding techniques have recently become important in a number of application areas. Digital audio, video, and pictures are increasingly furnished with distinguishing but imperceptible marks, which may contain a hidden copyright notice or serial number or even help to prevent unauthorized copying directly. Military communications systems make increasing use of traffic security techniques which, rather than merely concealing the content of a message using encryption, seek to conceal its sender, its receiver, or its very existence. Similar techniques are used in some mobile phone systems and schemes proposed for digital elections. Criminals try to use whatever traffic security properties are provided intentionally or otherwise in the available communications systems, and police forces try to restrict their use. However, many of the techniques proposed in this young and rapidly evolving field can trace their history back to antiquity, and many of them are surprisingly easy to circumvent. In this article, we try to give an overview of the field, of what we know, what works, what does not, and what are the interesting topics for research.

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Information hiding-a survey

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

This article reviews the requirements for successful compressed sensing (CS), describes their natural fit to MRI, and gives examples of four interesting applications of CS in MRI. The authors emphasize on an intuitive understanding of CS by describing the CS reconstruction as a process of interference cancellation. There is also an emphasis on the understanding of the driving factors in applications, including limitations imposed by MRI hardware, by the characteristics of different types of images, and by clinical concerns.

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Compressed Sensing MRI

A rate-distortion framework is used to define a very low-bit-rate coding scheme based on wireframe model adaptation and optimized selection of motion estimators. This technique achieves maximum reconstructed image quality under the constraint of a target bit rate for the coding of the vector field and the wireframe representation information. First, a complete scheme is proposed for hybrid two-dimensional (2D) and 3D motion estimation and compensation. The wireframe adaptation and updating is optimized for hybrid motion estimation in the rate distortion sense. A more sophisticated technique, adapted to the requirements of a very low-bit-rate coder is also proposed which considers also the transmission of the prediction error corresponding to the particular choice of the motion estimator for each object in the scene. Experimental results illustrating the performance of the proposed techniques in very low-bit-rate image sequence coding application areas are presented and evaluated. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 238–247, 1998

Optimization of wireframe model adaptation and motion estimation in a rate-distortion framework

In this paper, a graphical modeling-based approach to semantic video analysis is presented for jointly realizing modality fusion and temporal context exploitation. Overall, the examined video sequence is initially segmented into shots and for every resulting shot appropriate color, motion and audio features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing an initial association of each shot with the semantic classes that are of interest separately for every modality. Subsequently, an integrated Bayesian Network (BN) is introduced for simultaneously performing information fusion and temporal contextual knowledge exploitation, contrary to the usual practice of performing each task separately. The final outcome of the overall video analysis approach is the association of a semantic class with every shot. Experimental results as well as comparative evaluation from the application of the proposed approach in the domain of news broadcast video are presented.

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Combining multimodal and temporal contextual information for semantic video analysis

We develop a theory for immediately detecting patterns violating linear separability in a training set, as soon as they are presented to the classifier. The theory is based on the computation of the solution cone in the weight space of a single linear threshold unit (LTU). The separability-violating patterns can be skipped and in the end, we obtain a linearly separable subset of the original training set together with the subset's solution cone. We further propose an iterative algorithm for computing the solution cone and we introduce the concept of garbage collection for removing redundancy from the cone representation. The algorithm can be implemented by a novel neural network architecture where the synaptic weights have constant values throughout the life of the synapse. We show that by combining multiple such models we can construct a larger network capable of learning the classification boundaries of convex classes, therefore solving an important class of nonlinearly separable problems.

Neural nonlinear classifiers with synaptic weight commitment

Face and Facial Expression Recognition using Three Dimensional Data.

Efficient, disparity and motion compensated compression techniques are necessary for the transmission of 3DTV signals. Stereo vision geometry imposes certain coherence constraints between vector fields. The method proposed in this paper uses such a constraint in an effort to jointly optimize the four vectors involved in it. Both a candidate testing algorithm and a gradient-based method are tested for the optimization.

Michael G. Strintzis

Papers

Optimization of wireframe model adaptation and motion estimation in a rate-distortion framework

Combining multimodal and temporal contextual information for semantic video analysis

Neural nonlinear classifiers with synaptic weight commitment

Face and Facial Expression Recognition using Three Dimensional Data.

Combined Evaluation of Motion and Disparity Vector Fields for Stereoscopic Sequence Coding