NDLI: Application of reversible discrete-time cellular neural networks to image copyright labelling

Content Provider	IEEE Xplore Digital Library
Author	Tao Yang Crounse, K.R. Chua, L.O.
Copyright Year	1996
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA (Tao Yang)
Abstract	In this paper, we proposed a discrete-time cellular neural network (DTCNN) structure for the labelling of digital images. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary random image sequences. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Due to the reversibility of a reversible DTCNN, the same reversible DTCNN is used to recover the copyright label from a labelled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. Computer simulation results are presented.
Starting Page	19
Ending Page	24
File Size	359258
Page Count	6
File Format	PDF
ISBN	078033261X
DOI	10.1109/CNNA.1996.566482
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-24
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cellular neural networks Labeling Image sequences Computer networks Multimedia databases Space technology Cellular networks Application software Laboratories Digital images
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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