Electromagnetic Metamaterials: Transmission Line Theory and Microwave Applications. By Christophe Caloz and Tatsuo Itoh.

Character segmentation is a challenging problem in the field of optical character recognition. Presence of touched characters make this dilemma more crucial. The goal of this paper is to provide major concepts and progress in domain of off-line cursive touched character segmentation. Accordingly, two broad classes of technique are identified. These include methods that perform explicit or implicit character segmentation. The basic methods used by each class of technique are presented and the contributions of individual algorithms within each class are discussed. It is the first survey that focuses on touched character segmentation and provides segmentation rates, descriptions of the test data for the approaches discussed. Finally, the main trends in the field of touched character segmentation are examined, important contributions are presented and future directions are also suggested.

Methods and strategies on off-line cursive touched characters segmentation: a directional review

Graphics and Multimedia Department Faculty of Computer Science and Information systems University Technology Malaysia 81310 Skudai Johor Malaysia rkamjad@gmail.com Abstract : Character segmentation is a challenging problem in the field of optical character recognition. Touched characters make this dilemma more crucial. Accordingly, two broad classes of technique for touched character segmentation are identified. These include methods that perform explicit or implicit character segmentation. The basic methods used by each class of technique are presented and the contributions of individual algorithms within each class are discussed. It is the first survey that focuses on touched character segmentation and provides segmentation rates, descriptions of the test data for the approaches discussed. Finally, the main trends in the field of touched character segmentation are discussed and some important contributions are presented. Keywords : optical character recognition, touched character, documents analysis, character segmentation, explicit segmentation, implicit segmentation .

A Survey on Methods and Strategies on Touched Characters Segmentation

An audio encryption scheme based on Fast Walsh Hadamard Transform and mixed chaotic keystreams

In this paper, a novel scheme for secure audio transmission is introduced. The novelty of this scheme is that it combines four different techniques for audio encryption in the same scheme which makes it more secure: self-adaptive scrambling, multi chaotic maps, dynamic DNA encoding and cipher feedback encryption. Also, it introduces two new designed multi chaotic maps as pseudo random generators that combine five different chaotic maps with eight control parameters. The scheme consists of three phases with three secret keys. The first phase is a self-adaptive bit scrambling where SHA512 of the input audio is computed to be used as a first secret key for cyclic shifting the input audio binary stream which efficiently reduces the strong correlation between neighboring audio samples. The second phase is a dynamic DNA encoding of the scrambled audio by using a second secret key obtained from a pseudo random generator (LCS) using a novel design of multi chaotic maps including Sine, Chebyshev and Logistic map with three control parameters. As DNA encoding provides fast computations and large capacity for data transmission, the third phase consists of two DNA algebraic operations “AND” and “XOR” using a pseudorandom DNA sequence as a third secret key generated by another new designed multi chaotic HLG map which combines Henon, Logistic and Gaussian chaotic maps and has five control parameters. The third phase is structured in a cipher feedback mode and so it achieves strong diffusion and confusion in the encrypted audio. Also combining five different chaotic maps in the proposed scheme increases the number of secret control parameters which achieves a huge key space and so robust strength against brute force attacks. The scheme is evaluated by using different measurements including signal to noise ratio (SNR), peak signal to noise ratio (PSNR), number of samples change rate (NSCR), unified average changing intensity (UACI), root mean square (RMS), crest factor (CF), correlation coefficient, histogram, key sensitivity and key space. From results, it is clear that the scheme is highly secure and stronger than many recent similar audio encryption schemes against different types of attacks.

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Audio Encryption Scheme Using Self-Adaptive Bit Scrambling and Two Multi Chaotic-Based Dynamic DNA Computations

This paper proposes a new approach to the encryption of speech intended to be transferred on an insecure channel. The proposed technique is based on stream encryption where the original speech is pre-processed in order to select the relevant data for encryption by removing the unvoiced parts of the signal. In addition to reducing the size of the speech signal, the selection process also reduces the encryption and decryption times. The selected data is encrypted using a pseudo-random generator to make the information unable to understand by an intruder. The used pseudo-random generator is based on two 256-bits shift registers, one is linear and the other non-linear. This generator allows encryption in higher dimensional space which expands the key space and, therefore, improves security against brute force attack. Various analyzis such as histograms, key space, key sensitivity, correlation, brute force attack, signal to noise (SNR) and peak signal to noise ratio (PSNR) have been performed. The analysis shows that the proposed scheme is computationally simple, efficient with superior performance compared to other systems presented in the literature.

A novel approach based on stream cipher for selective speech encryption

Automatic reading of numeric chains has been attempted in several application areas such as bank cheque processing, postal code recognition and form processing. Such applications have been very popular in handwriting recognition research, due to the possibility to reduce considerably the manual effort involved in these tasks. In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, study was based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. Used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: distribution sequence, Barr features and centred moments of different projections and profiles. Secondly, study was extented for the reading of the handwritten numeric chains constituted of a variable number of digits. Vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The performances of the proposed system for the used database attain a recognition rate equal to 91.3%.

Segmentation and Recognition of Handwritten Numeric Chains

In this paper, a novel system is proposed for automating the process of brain tumor classification in magnetic resonance (MR) images. The proposed system has been validated on a database composed of 90 brain MR images belonging to different persons with several types of tumors. The images were arranged into 6 classes of brain tumors with 15 samples for each class. Each MR image of the brain is represented by a feature vector composed of several parameters extracted by two methods: the image entropy and the seven Hu's invariant moments. These two methods are applied on selected zones obtained by sliding a window along the MR image of the brain. The size of the used sliding window is 16x16 pixels for the first method (image entropy) and 64x64 pixels for the second method (seven Hu's invariant moments). To implement the classification, a multilayer perceptron trained with the gradient backpropagation algorithm has been used. The obtained results are very encouraging; the resulting system properly classifies 97.77% of the images of the used database.

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Novel Method for Brain Tumor Classification Based on Use of Image Entropy and Seven Hu’s Invariant Moments

The automatic verification of kinship is a challenging problem that recently attracted much interest in computer vision, the kinship verification has become an active research field due to its potential applications such as organizing photo albums and images annotation, recognizing resemblances among humans and finding of missing children. In this paper, we propose an approach which takes two images as an input then give kinship result (kinship / non-kinship) as an output.This approach based on the Local Phase Quantization (LPQ) and Local directional pattern (LDP) features descriptors and the ML (Multi-Level) representation for the kinship verification from facial images, this work consists six stages which are : (i) face preprocessing, (ii) features extraction, (iii) face representation (iv) pair features representation and normalization, (v) features selection and (vi) kinship verification. Experiments are conducted on four public databases (Cornell KinFace, UB Kin database, KinFace-I, and KinFace-II). The obtained results are good compared with state-of-the-art approaches.

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Salim Ouchtati

Papers

A novel approach based on stream cipher for selective speech encryption

Segmentation and Recognition of Handwritten Numeric Chains

Segmentation and Recognition of Handwritten Numeric Chains

Novel Method for Brain Tumor Classification Based on Use of Image Entropy and Seven Hu’s Invariant Moments

LPQ and LDP Descriptors with ML Representation for Kinship verification