International Journal of Computer and Applications

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

On discrete cosine transform

Dans cet article, nous presentons l'architecture de MADKIT (pour "Multi-Agent Development Kit"), une plate-forme generique de conception et d'execution de systemes multi-agents. Cette plate-forme a l'originalite d'etre base sur un modele organisationnel plutot qu'une architecture d'agent ou un modele d'interaction specifique. L'utilisation de groupes et de roles associes a des agents est mis en oeuvre en tant qu'outil de modelisation et de conception pour les developpeurs de systemes multi-agents, mais egalement comme principe d'architecture de la plate-forme elle-meme. Cette architecture est basee sur un noyau agent minimal decouple de tout modele individuel d'agent. Dans cette plate-forme, les services classiques de passage de message distribues, de migration ou de surveillance sont fournis au meta-niveau par des agents specialises afin d'ob-tenir un maximum de flexibilite. Une interface graphique componentielle et decouplee du noyau et des agents permet de supporter differentes modes d'utilisation et d'exploitation de la plate-forme. Nous illustrons notre propos en presentant certaines consequences, declinaisons et applications de cette plateforme.

https://hal.umontpellier.fr/hal-02403991/document

MADKIT: une expérience d'architecture de plateforme multi-agent générique

A stochastic multi-agent approach for medical-image segmentation: Application to tumor segmentation in brain MR images

Medical image segmentation is a difficult task, essentially due to the inherent complexity of human body structures and the acquisition methods of this kind of images. Manual segmentation of medical images requires advance radiological expertize and is also very time-consuming. Several methods have been developed to automatize medical image segmentation, including multi-agent approaches. In this paper, we propose a new multi-agent approach based on a set of autonomous and interactive agents that integrates an enhanced region growing algorithm. It does not require any prior knowledge. This approach was implemented and experiments were performed on brain MRI simulated images and the obtained results are promising.

Towards a Generic Multi-agent Approach for Medical Image Segmentation

This article presents a multi-agent approach for the segmentation of images. A multi-agent system (MAS) is a distributed system consisting of a set of agents that interact with themselves in an environment they are able to perceive and on which they can act. The proposed solution consists in cutting the space of the image to treat it in a set of sub-spaces (partitions of the image) in which several agents are created to detect the outlines of objects then to follow them (these agents are called detector – followers agents). These agents adapt a very efficient algorithm of detection and follow the outline according to the characteristics of the region that they evolve in. The information so collected is transmitted to levels of supervision agents (agents partitions) which take care they with collecting the information emitted by the agents detector - followers, to update tables containing the parameters of segmentation and to elaborate global strategies of management of the agents detector - followers (creation, destruction, setting in a stand-by mode or initialization of agents detector-followers) . At the highest level of this agent’s hierarchy, we find the supervisor agent of this whole system. An implementation of this approach by the use of Madkit system allowed us to observe a gain in performances and in precision very important due to parallel, concurrent and cooperating execution of tasks.

/pdf/an-adaptative-multi-agent-system-approach-for-image-5bx5vjfdv0.pdf

An Adaptative Multi-agent System Approach for Image Segmentation

describe, in this paper, a multi-agent system (MAS) dedicated to the compression of digital images. An Image may be considered as a matrix of N lines and of M columns, that is to say a size of NxM pixels. To each line, we associate an agent. Our approach is inspired from the RLE (Run Length Encoding) method but instead of storing the occurrences of the pixels having a same value followed by the value of these pixels, we use a labeling table which contains all the values of the pixels of the image. We obtain a compressed image as a list of color labels followed by the number of successive occurrences of this color. We observe, that, an image is made up of about thirty different colors approximately, this mean that the label of color can be coded on five bits. This method is very effective for images where the color is coded on several bytes. The algorithm is as follow: each agent traverses the image line with which it is associated by seeking for each detected color the number of its successive occurrences. The agent thus draws up a set of lists for each color found including the number of its occurrences. All the other agents carry out these tasks in parallel. It is possible to obtain images compressed without or with loss of information according to the desired quality of the compressed image. This approach allows a very great improvement of the performances in storage capacity of information (the compressed images thus obtained occupy less memory space) and in execution time (the parallel execution allows very high accelerations of the algorithms).

/pdf/a-multi-agent-system-for-image-compression-7b3btlzoz7.pdf

Redjimi Mohammed

Papers

An Adaptative Multi-agent System Approach for Image Segmentation

A Multi-agent System for Image Compression