DocumentCode
1574481
Title
An object-oriented approach to video coding via the CNN Universal Machine
Author
Toffels, A. ; Roska, Tamáis ; Chua, Leon O.
Author_Institution
Inst. of Power Syst. & Power Econ., Tech. Hochschule Aachen, Germany
fYear
1996
Firstpage
13
Lastpage
18
Abstract
The cellular neural network Universal Machine (CNNUM) is applied to object-oriented image compression algorithms and proves its universality as a hardware platform for future applications. The estimated processing times allow a real-time analysis of the video sequence and outdo the performance of other comparable digital devices reported
Keywords
cellular neural nets; computer vision; data compression; image segmentation; image sequences; object-oriented methods; video coding; CNN Universal Machine; CNNUM; cellular neural network; image compression; object labelling; object-oriented method; processing times; real-time analysis; segmentation; universality; video coding; video sequence; Cellular neural networks; Image coding; Image segmentation; Labeling; Power engineering computing; Power system economics; Turing machines; Video coding; Video compression; Video sequences;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on
Conference_Location
Seville
Print_ISBN
0-7803-3261-X
Type
conf
DOI
10.1109/CNNA.1996.566481
Filename
566481
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