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」

Image processing with neural networks–a review

A new class of Zernike moments for computer vision applications

A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

Fuzzy Cognitive Maps for Pattern Recognition Applications

An image encryption based on elementary cellular automata

Shape recognition with a neural classifier based on a fast polygon approximation technique

http://chatzichristofis.info/files/papers/J4.pdf

A novel cellular automata based technique for visual multimedia content encryption

A neural multiclassifier system for object recognition in robotic vision applications

Publisher Summary This chapter proposes a high performance neural multi-classifier system (NEMUS) that is characterized by a great degree of modularity and flexibility. It is very efficient for demanding and generic pattern recognition applications. The NEMUS is composed of two stages. The first stage is comprised of several classifiers that operate in parallel, while the second stage is a decision-making network (DM-Net) that performs the final classification task, combining the outputs of all the classifiers of the first stage. In general, the inputs of each classifier are the features extracted from different feature extraction methods and correspond to various levels of importance. The performance of the proposed NEMUS is demonstrated by a shape recognition task of 2-D digitized objects, considering various levels of shape distortions. Three different kinds of features, which characterize a digitized object, are used: geometric features, 1-D scaled normalized central moments, and the angles of a fast polygon approximation method.

A high performance neural multiclassifier system for generic pattern recognition applications

A novel methodology is herein presented for combining the decisions of different feedforward neural network classifiers. Instead of the usual approach of applying voting schemes on the decisions of their output layer neurons, the proposed methodology integrates the higher order features extracted by their upper hidden layer units through a second stage feedforward neural network having as inputs all such higher order features. Therefore, an hierarchical neural system for pattern recognition has been developed with improved classification performance. The validity of this novel combination approach has been investigated when the first stage neural classifiers involved correspond to different Feature Extraction Methodologies (FEM) for shape classification. The experimental study illustrates that such an approach, integrating higher order features into a second stage feedforward neural classifier, outperforms other combination methods, like voting combination schemes as well as single neural network classifiers having as inputs all FEMs derived features.

Dimitris A. Mitzias

Papers

Shape recognition with a neural classifier based on a fast polygon approximation technique

A novel cellular automata based technique for visual multimedia content encryption

A neural multiclassifier system for object recognition in robotic vision applications

A high performance neural multiclassifier system for generic pattern recognition applications

A Robust Hierarchical Neural Network Methodology for Improved Image Classification Performance