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We describe a new watermarking system based on the principles of informed coding and informed embedding. This system is capable of embedding 1380 bits of information in images with dimensions 240 x 368 pixels. Experiments on 2000 images indicate the watermarks are robust to significant valumetric distortions, including additive noise, low-pass filtering, changes in contrast, and lossy compression. Our system encodes watermark messages with a modified trellis code in which a given message may be represented by a variety of different signals, with the embedded signal selected according to the cover image. The signal is embedded by an iterative method that seeks to ensure the message will not be confused with other messages, even after addition of noise. Fidelity is improved by the incorporation of perceptual shaping into the embedding process. We show that each of these three components improves performance substantially.

Searching online information resources using mobile devices is affected by small screens which can display only a fraction of ranked search results. In this paper we investigate whether the search effort can be reduced by means of a simple user feedback: for a screenful of search results the user is encouraged to indicate a single most relevant document. In our approach we exploit the fact that, for small display sizes and limited user actions, we can construct a user decision tree representing all possible outcomes of the user interaction with the system. Examining the trees we can compute an upper limit on relevance feedback performance. In this study we consider three standard feedback algorithms: Rocchio, Robertson/Sparck-Jones (RSJ) and a Bayesian algorithm. We evaluate them in conjunction with two strategies for presenting search results: a document ranking that attempts to maximize information gain from the user's choices and the top-D ranked documents. Experimental results indicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximizes information gain produces better results. We conducted a user study to compare the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behavior indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. [ABSTRACT FROM AUTHOR]

Proposes a watermarking algorithm that is robust to rotation, scaling, and/or translation distortion of an image. Algorithm description; Implementation problems and solutions; Experimental results.

Compares the two algorithms for determining ranked assignments with the application to multitarget tracking. Danchick and Newman algorithm; Experimental comparison; Conclusion of the study.

Focuses on a secure (tamper-resistant) algorithm for watermarking images, and a methodology for digital watermarking which may be generalized to audio, video and multimedia data. Construction of a watermark as an independent and identically distributed; Information on Gaussian random vector which is inserted in a spread-spectrum-like fashion.