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Research

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Computer Sciences Department of the University of Salzburg headed by Andreas Uhl. The short name "WaveLab" already indicates that wavelets are among our favorite tools - we have 15 years of experience in this area. Our research is focused on Multimedia Security including Watermarking, Image and Video Compression, Medical Image Classification, and Biometrics.

Media Security & Watermarking

Description:

In the area of media encryption we are specializing in lightweight and partial encryption schemes for image and video data with special emphasis on scalable media like JPEG2000 or H.264 SVC. In the watermarking area our focus is on developing key-dependency schemes for robust embedding techniques. Scalable watermarking and multiple watermarking are recent topics of reasearch. In robust hashing, key-dependent wavelet transforms are investigated as a means to provide security for such schemes.

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Image- and Videocoding

Description:

We have been specializing in image and video compression techniques involving wavelets for over 10 years now. Emphasis has been given to adaptive techniques like wavelet packets or object based coding inspired by MPEG-4. Also, cache efficient and parallel algorithms have been developed for a number of corresponding algorithms. More recently, we concentrate on scalable coding schemes like JPEG2000, MCTF scalable wavelet codecs, and H.264 SVC.

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Recent Publications...

 

Biometrics

Description:

We focus on biometric modalities where image processing is conducted during feature extraction and template generation (i.e. fingerprints, iris, face, retina, hand and foot geometry). Current emphasis of our work is on hand- and footgeometry, on sample data compression and encryption, as well as on privacy in biometrics (like cancelable biometrics).

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Recent Publications...

 

Medical Image Classification

Description:

Together with partners from different medical departments, we apply classification techniques to medical image data targeted towards the development of computer-based decision support systems for endoscopic imagery. On the one hand, classical texture classification features are used, on the other hand we develop new schemes and customize them to the target imagery. Currently, we work on a staging technique for colon cancer and diagnosis of celiac disease.

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Recent Publications...