
Short ProgramDetails and program modifications will be announced and made available
at the conference web site at:
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Thursday, May 31
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Friday, June 1
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Saturday, June 2
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Sunday, June 3
*The concurrent sessions will be from peer reviewed papers in the areas of intelligent multimedia and distance education, contributed by international participants representing more than 20 countries around the world. | ||||||||||||||||||||||||||||||||||||
TutorialsMultimedia Streaming: MPEG-4 ApproachWael Badawy MPEG-4 is a new ISO/IEC standard that targets streaming multimedia. The MPEG (Moving Picture Experts Group) developed MPEG-1 and MPEG-2 that target interactive video on CD-ROM and Digital Television, respectively. The MPEG-4 provides tools to deliver multimedia content over different communication channels and targets a wide range of interactive applications. MPEG-4 provides new features such as scalability which enable the providers to transmit the same content using different channels, error-resilient techniques that allow robust transmission over wireless transmission, content-based approach that allows higher levels of interaction with scene contents. The MPEG-4 scene is presented as aural, visual or audiovisual objects or AVOs. The MPEG-4 became an International Standard for multimedia streaming in January 1999 and its second version, as an amendment to Version 1, achieved the status of 'Final Draft International Standard' in December 1999. This tutorial will introduce the MPEG-4 Version 1 and Version 2 visual coding tools and their functionalities. It will focus on the applications of MPEG-4 for multimedia streaming and how wired and wireless multimedia streaming can benefit from MPEG-4 visual coding tools. Tutorial Topics:
The tutorial will target audience from several backgrounds. Image and Video Compression Techniques & StandardsS. R. Subramanya The phenomenal increases in the generation, transmission, and use of digital images and video in many applications is placing enormous demands on the storage space and communication bandwidth. Data compression is a viable approach to alleviate the storage and bandwidth demands. This tutorial is intended to give an insight into a few major image and video compression techniques and a brief look at the popular image and video coding standards. The tutorial starts with an overview of the basics of lossy compression techniques in general. The concepts and issues in quantization are introduced. Predictive image coding techniques are then covered, which primarily includes DPCM and ADPCM. Transform coding techniques using DFT and DCT are then addressed. A brief overview of Subband coding is then given, followed by Vector Quantization. The bi-tonal image compression standard JBIG, and continuous-tone image standard JPEG are then covered. Video coding techniques is the next topic which covers elements of motion estimation and motion compensation. This is followed by the motion video standard MPEG, and videoconferencing/videotelephony standards H.261/263. This tutorial enables the participant to:
Topics Covered:
It is Time to Fuzzify Neural NetworksAjith Abraham Neural Networks and fuzzy inference systems have been widely used in several intelligent multimedia applications. Artificial Neural Network learns from scratch by adjusting the interconnections between layers. A valuable property of neural network is that of generalization, whereby a trained network is able to provide a correct matching in the form of output data for a set of previously unseen input data. Fuzzy Inference System is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. With crisp inputs and outputs, fuzzy inference system implements a nonlinear mapping from its input space to output space by a number of if-then rules. Integrating Neural Networks and Fuzzy Inference Systems have attracted the growing interest of researchers in various multimedia applications due to the growing need of adaptive intelligent systems to meet the real world requirements. There are several approaches to integrate neural networks and fuzzy inference systems and very often it depends on the application. We broadly classify the integration of neuro fuzzy systems into three categories namely concurrent model, cooperative model and fully integrated model. We briefly discuss the features of each model and generalize the advantages and deficiencies of each model. We will also attempt to give some insights when to use which model. This tutorial starts with some basic theoretical aspects of neural networks and fuzzy inference systems and their application areas stressing the advantages of each technique. We further discuss the step-by-step modeling of different neuro-fuzzy architectures. We also demonstrate how neuro-fuzzy techniques could be used for many practical applications involving image classification and data mining tasks etc.
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