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

The detection of lumen and media-adventitia borders in intravascular ultrasound (IVUS) images constitutes a necessary step for accurate morphometric analyses of coronary plaques and accordingly assessment of the atherosclerotic lesion length. Aiming to tackle this issue, an automated model for lumen and media-adventitia border detection is presented, which is based on active contour models. The proposed approach enables extraction of the corresponding boundaries in sequential IVUS frames by applying an iterative procedure, in which initialization of the two contours in each frame is performed automatically, based on the segmentation of its previous frame. The above procedure is implemented through a user-friendly interface, permitting the interaction of the user when needed. The in vivo application and evaluation of our model in sequential IVUS images indicated that the proposed approach is capable of accurately and rapidly segmenting hundreds of IVUS images.

An automated model for rapid and reliable segmentation of intravascular ultrasound images

Machine classification of ishemic electrocardiograms

In this paper a novel blind watermarking method of 3D volumes based on the Weighted 3D Krawtchouk Moments is proposed. The watermark is created by a pseudo-random number generator and is embedded on low order Weighted 3D Krawtchouk Moments. The watermark detection is blind, requiring only the user’s key. The watermark bit sequence is created using the key and its cross correlation with the Weighted 3D Krawtchouk Moments of the possible watermarked volume. The proposed method is imperceivable to the user, robust to geometric transformations (translation, rotation) and to cropping attacks.

3d volume watermarking using 3d krawtchouk moments

The segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of learning vector quantizer (called L/sub 2/ LVQ) is proposed so that the weight vectors of the output neurons correspond to the L/sub 2/ mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed variant of LVQ is studied. Experimental results show that L/sub 2/ LVQ outperforms other segmentation techniques that employ thresholding a filtered ultrasonic image with respect to the probability of detection for the same probability of false alarm in all cases. >

A variant of learning vector quantizer based on the L/sub 2/ mean for segmentation of ultrasonic images

A new technique for three-dimensional image registration was developed and tested using T1 and T2 weighted Magnetic Resonance image studies of the head. The method uses the fuzzy c-means classification algorithm for outlining the surface contours and then minimizes iteratively the mean squared value of the voxel per voxel weighted ratio of the two trilinearly interpolated cubic voxel volumes. A total of 200 two-dimensional and 240 three-dimensional registration experiments were performed and showed that the method is signal intensity independent, it has registration accuracy better than 1 degree for rotations and 1 voxel for translations and it is not affected by the deterioration in the imaging resolution for voxel sizes up to 1.8 mms.

Michael G. Strintzis

Papers

An automated model for rapid and reliable segmentation of intravascular ultrasound images

Machine classification of ishemic electrocardiograms

3d volume watermarking using 3d krawtchouk moments

A variant of learning vector quantizer based on the L/sub 2/ mean for segmentation of ultrasonic images

A new automated method for three-dimensional registration of MR images of the head.