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Advancements in Sonar image capture have opened the door to powerful classification schemes for automatic target recognition (ATR). Recent work has particularly seen the application of sparse reconstruction-based classification (SRC) to sonar ATR, which provides compelling accuracy rates even in the presence of noise and blur. However, existing sparsity based sonar ATR techniques assume that the test...
The problem of resolution enhancement in images from multiple low-resolution captures has garnered significant attention over the last decade. While initial algorithms estimated the unknown high-resolution (hi-res) image for a fixed set of imaging model parameters, significant recent advances have been in simultaneous maximum aposteriori (MAP) estimation of the hi-res image as well as the geometric...
Robust image hashing has been actively researched over the last decade with varied applications in image content authentication and identification under distortions. In the existing literature on robust image hashing, hash algorithms are ignorant of the class of images being hashed. There are however significant application domains such as that of face image hashing where apriori knowledge of the...
This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers...
Halftone image watermarking techniques that allow automated extraction of the embedded watermark data are useful in a variety of document security and workflow applications. The print-scan process inherent in these applications introduces distortions whose characteristics exhibit a strong spatial dependence on the cover image in which the data is embedded. In this paper, we demonstrate that a characterization...
Clustered-dot halftones are extensively utilized in hardcopy printing. Modulation of the dot orientation in these halftones offers an avenue for data embedding which has been exploited in a number of different methods. We consider the capacity of these channels, modeling them as binary orientation input channels with vector valued output detection statistics. We derive upper bounds on the capacity...
In this paper, we propose the use of non-negative matrix factorization (NMF) for image hashing. In particular, we view images as matrices and the goal of hashing as a randomized dimensionality reduction that retains the essence of the original image matrix while preventing intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as...
In this paper, we propose the use of non-negative matrix factorization (NMF) for robust image hashing. In particular, we view images as matrices and the goal of hashing as a randomized dimensionality reduction that retains the essence of the original image matrix while preventing against intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques...
Surviving geometric attacks in image authentication is considered to be of great importance. This is because of the vulnerability of classical watermarking and digital signature based schemes to geometric image manipulations, particularly local geometric attacks. In this paper, we present a general framework for image content authentication using salient feature points. We first develop an iterative...
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