Abstract not found

Abstract not found

photocopy or other means, without the permission of the author. ii

Abstract—Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals are discrete-time sequences that at, any particular index, have a value that is a member of a finite set of symbols. Set membership defines the only mathematical structure that symbolic sequences satisfy. Consequently, symbolic signals cannot be directly processed with existing signal processing algorithms designed for signals having values that are elements of a field (numeric signals) or a group. Generalizing an approach due to Stoffer, we extend time–frequency and time-scale analysis techniques to symbolic signals and describe a general linear approach to developing processing algorithms for symbolic signals. We illustrate our techniques by considering spectral and wavelet analyses of DNA sequences. Index Terms—DNA sequences, linear transforms, symbolic signal. I.

A robust watermarking scheme for hiding binary or gray-scale watermarks in digital images is proposed in this chapter. Motivated by the fact that a detector response (a correlation value) only provides a soft evidence for convincing jury in courtroom, embedded watermarks are designed to be visually recognizable after retrieval. To strengthen the existence confidence of a watermark, visually significant transformed components are selected. In addition, a relocation technique is presented to tackle geometric-distortionbased attacks without using any registration scheme. Finally, a semi-public watermark detector which does not require use of the original source is proposed for the purpose of authentication. Experimental results demonstrate that our approach satisfies the common requirements of image watermarking, and that the performance is superb. Keywords: Human visual system Wavelet transform Watermarking Modulation Attacks Corresponding author 1 INTRODUCTION 1.1 WATERMARKING Ow...

In this paper we give a brief introduction to lter banks over commutative rings. In contrast to lter banks over the real numbers, we employ nite ring arithmetic to control the number of bits in the signal representations. This way we avoid the coecient swell problem that is preeminent in rings of characteristic zero. We derive decompositions for images that are tailored to dedicated hardware implementations. These decompositions reduce the size of linebu ers which dominate the silicon area in integrated circuit implementations. As an application, we derive a lossless compression scheme for 8 bit monochrome images using wavelet lters with values in the ring Z=256Z. Keywords: wavelets, commutative rings, lossless image compression, integrated circuits 1.

Characterization of halftone texture is important for quantitative assessment of halftone quality. In this paper, we develop a new framework based on directional local sequency analysis and a filter bank structure. We decompose a halftone image into subband images from which we can easily reconstruct the original halftone. Based on these subband images, we define the directional sequency spectrum which is analogous to the 2-D Fourier spectrum, and formulate several texture measures. Three test images are used to justify these measures. 1.

Processing of symbolic sequences represented by mapping of symbolic data into numerical signals is commonly used in various applications. It is a particularly popular approach in genomic and proteomic sequence analysis. Numerous mappings of symbolic sequences have been proposed for various applications. It is unclear however whether the processing of symbolic data provides an artifact of the numerical mapping or is an inherent property of the symbolic data. This issue has been long ignored in the engineering and scientific literature. It is possible that many of the results obtained in symbolic signal processing could be a byproduct of the mapping and might not shed any light on the underlying properties embedded in the data. Moreover, in many applications, conflicting conclusions may arise due to the choice of the mapping used for numerical representation of symbolic data. In this paper, we present a novel framework for the analysis of the equivalence of the mappings used for numerical representation of symbolic data. We present strong and weak equivalence properties and rely on signal correlation to characterize equivalent mappings. We derive theoretical results which establish conditions for consistency among numerical mappings of symbolic data. Furthermore, we introduce an abstract mapping model for symbolic sequences and extend the notion of equivalence to an algebraic framework. Finally, we illustrate our theoretical results by application to DNA sequence analysis. Index Terms Transform equivalence; DNA sequence analysis; symbolic signal processing. A version of this manuscript has been accepted for publication in the IEEE Transactions on Signal Processing, 2009.

In this paper, perfect reconstruction filter bank structures for GF-(N) fields are developed. The new filter banks are based on the nonlinear subband decomposition and they are especially useful to process binary images such as document

Abstract—In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented. Index Terms — Binary image coding, binary subband decomposition, document retrieval, textual image compression. Fig. 4. Percentage increase of SNRS bit rate over equivalent single-layer versus qe, with index ratio qb =qe = 2:4.