Read or Download Digital Watermarking Algorithms Robust Against Loss of Synchronization PDF
Best algorithms and data structures books
This Little information ebook provides at-a-glance tables for over a hundred and forty economies displaying the newest nationwide info on key signs of data and communications know-how (ICT), together with entry, caliber, affordability, efficiency,sustainability, and purposes.
Media student ( and web fanatic ) David Shenk examines the troubling results of data proliferation on bodies, our brains, our relations, and our tradition, then bargains strikingly down-to-earth insights for dealing with the deluge. With a skillful mix of own essay, firsthand reportage, and sharp research, Shenk illustrates the significant paradox of our time: as our global will get extra advanced, our responses to it turn into more and more simplistic.
Franca Piazza untersucht auf foundation der Entscheidungstheorie das Einsatzpotenzial von facts Mining im Personalmanagement. Sie zeigt, welche personalwirtschaftlichen Entscheidungen unterstützt werden können, worin der Beitrag zur personalwirtschaftlichen Entscheidungsunterstützung besteht und wie dieser zu bewerten ist.
- Genetic Algorithms + Data Structures = Evolution Programs
- Exploratory Analysis of Metallurgical Process Data with Neural Networks and Related Methods
- Evolutionary Robotics: From Algorithms To Implementations (World Scientific Series in Robotics and Intelligent Systems)
- essential books on algorithms and data structures
Additional info for Digital Watermarking Algorithms Robust Against Loss of Synchronization
A Random repetition (expansion) ✝✆ Sequence S ~ A A Pattern construction ~ A A ... ✂✁ ☎ ✄ , Pattern W ( ✞✠✟☛✡☞✞✍✌✎✡☞✏ ; ✑✓✒✔✡✖✕✘✗ ; ✙✛✚✜✡☞✢ ; ✙✤✣✥✡✦✕✧✏ ) ~ ~ A A ~ ~ A A A A A A ... ~ ~ A A ~ ~ A A ... A A A A ... ... ... ... 7: Watermark pattern construction parameters should be optimized for each situation. 38 Chapter 3. 12 is motivated by the will to minimize the image degradation while maximizing the detection reliability. This leads to the shaping of the watermark signal to fit image characteristics.
In our scenario, the different detection values ✥ ✼ are derived combining all realizations from the fixed set of✠ realizations in . For this✞ reason, distribution of ✏ ✼ for varying shifts ✞ will not exactly fit the ✔ distribution. The way that it will differ from ✔ distribution is described below. 25) ✞ In our case, distribution ✑ ✒ differs from the ✔ distribution in accordance ☞ ❍ with the values of and . One can show that the first moment will still be but the variance will take values comprised between 0 and ✫✄ ☎✢ ✞ ☞ depending on the ratio .
In order to address more complex transform while maintaining reasonable computational cost, detection can sometimes be performed on smaller regions of the image [29, 30] provided sufficient watermark-to-image power ratio is available. Strong hypotheses can be made on the relative continuity or smoothness of the deformation, further reducing the search space . Another mean to reduce search space is to use periodically structured watermarks [32, 33, 34]. It enables to limit search for synchronization over one repetition period.