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This paper describes a relational graph matching with model-based segmentation for human detection. The matching result is used for the decision of human presence in the image as well as for posture recognition. We extend our previous work for rigid object detection in still images and video frames by modeling parts with superellipses and by using multi-dimensional Bayes classification in order to determine the non-rigid body parts under the assumption that the unary and binary (relational) features belonging to the corresponding parts are Gaussian distributed. The major contribution of the proposed method is to create automatically semantic segments from the combination of low level edge or region based segments using model-based segmentation. The generality of the reference model part attributes allows detection of human with different postures while the conditional rule generation decreases the rate of false alarms.

Blind or oblivious data hiding, can be considered as a signaling method where the origin of the signal constellation is not known. The origin however, can be estimated, by means of self-noise suppression techniques. In this paper, we propose such a technique, and present both theoretical and numerical evaluations of its performance in an additive noise scenario. The problem of optimal choice of the parameters of the proposed technique is also explored, and solutions are presented. Though the cover object is assumed to be an image for purposes of illustration, the proposed method is equally applicable for other types of multimedia data, like video, speech or music.

We propose a novel, robust scheme for data hiding/oblivious detection of watermarks in still images. While the low- frequency image coefficients are robust, they cannot be used effectively for oblivious detection methods, when correlative processing is employed for detection. However, in the proposed non-linear detection method, the robust low- frequency bands can be used effectively. Thus the proposed method turns out to be more robust than methods employing linear addition and correlative extraction of the signature. We report the results obtained for 7 test images in terms of probability of error in detection of the watermark/hidden bits.

We present an information-theoretic approach to obtain an estimate of the number of bits that can be hidden in still images, or, the capacity of the data-hiding channel. We show how the addition of the message signal or signature in a suitable transform domain rather than the spatial domain can significantly increase the channel capacity. Most of the state-of-the-art schemes developed thus far for data-hiding have embedded bits in some transform domain, as it has always been implicitly understood that a decomposition would help. In this paper we compare the achievable data-hiding capacities for different decompositions like DCT, Hartley, Hadamard, and subband transforms. We show that transforms with inferior energy compaction property like Hartley and Hadamard are better choices for the decomposition, than transforms with good energy-compaction property, like DCT or subband transforms.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We present a data hiding scheme for still images, in which only the magnitude of the DFT coefficients are altered to embed the hidden information bits. We begin by identifying various components of a typical data hiding scheme, like the decomposition used, signature design, self-noise suppression and signaling. The final choice of components for the proposed data hiding scheme are well tailored by utilizing theoretical reasonings and experimental observations. Performance results show good robustness of the data hiding scheme for both JPEG and SPIHT image compression.

We present an information-theoretic approach to obtain an estimate of the number of bits that can be hidden in compressed image sequences. We show how addition of the message signal in a suitable transform domain rather than the spatial domain can significantly increase the data hiding capacity. We compare the data hiding capacities achievable with different block transforms and show that the choice of the transform should depend on the robustness needed. While it is better to choose transforms with good energy compaction property (like DCT, wavelet etc.) when the robustness required is low, transforms with poorer energy compaction property (like the Hadamard or Hartley transform) are preferable choices for higher robustness requirements.

As noted in Chapter 1, a major thrust of research in subband filter banks has focussed on methods for achieving perfect reconstruction (PR), defined as the property wherein the reconstructed output signal is simply a delayed version of the input signal. There are several established categories of PR filter banks, the most popular of which are the paraunitary and the biorthogonal varieties, as well as a spate of others [7, 418, 430, 371]. While all of these insure PR, there is literally an infinite number of solutions, even for the simplest of filter structures. The space of feasible solutions can be narrowed by design procedures that optimize certain objective measures while maintaining perfect reconstruction as a constraint.

The ten papers in this special issue focus on the topic of signal processing applications and methodologies used in the finance and electronic trading industries. This issue compiles such research contributions in financial applications coming from the fields of mathematics, finance, and engineering in order to foster future scientific cross-fertilization.

This paper shows that performance improvements are possible if different filter banks in the different levels, even in the nodes, of a subband tree are used. A progressive optimization technique is proposed for fine tuning of the product subband filters in a tree structure. The time and frequency properties of product subband filters are evaluated and comparisons made.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper provides an overview of subband and wavelet theories. It emphasizes their strong relations. The practical merits of these decomposition techniques in signal processing are examined. The current status of this active research field is summarized and it concludes with the discussion of potential extensions for future study.

This paper compares the objective and subjective performance of direct and hierarchical subband decomposition schemes for image coding. It is observed that the hierarchical subband schemes outperform the direct structures in image coding. It is also shown that the dyadic tree subband codec outperforms the full-tree case for the image coding experiments performed in this study.

The authors derive the discrete-time uncertainty and evaluate the time-frequency localizations of orthogonal bases. They measure and compare the localization features of known block transforms, filter banks, and wavelet filters. It is emphasized that the time and frequency behaviors of basis functions should be considered simultaneously in the design of block transforms and filter banks for further improvements in their applications. >

This paper searches for the suboptimal multiplierless orthonormal PR-QMF solutions. It isshown that there are multiplierless PR-QMFs and they perform comparable or better than the known filter banks and DCT based codec for the objective and subjective tests performed. They arevery efficient to implement on VLSI. It is expected that these PR-QMFs will find their applicationsin real-time image-video coding. I. INTRODUCTION The recent research has shown that the block transforms and filter banks have the same origin of linear transformations[1]{2}. The main difference of these decomposition techniques is the durations of the functions in their transform bases. This fact leads to different sets of orthogonality conditions for each one. The filter banks are more adequate than block transforms for the multiresolution property which imposes additional conditions of factorizability in the basis design[3}[4}[5][6}{7]. The optimal block transform, Karhunen-Loeve Transform(KLT), is unique for the given inputstatistics and the transform size. The KLT decorrelates the signal perfectly and the diagonal natureof its coefficient covariance matrix assures the optimality of the basis also for the energy compactionmeasure. It is obvious that the KLT is signal dependent and not used in applications. The fixedblock transforms like Discrete Cosine(DCT), Discrete Sine(DST), Walsh-Hadamard(WHT), Mod-ified Hermite(MHT) and others have been employed in practice and shown that the DCT is thesuperior one. The DCT is the decomposition technique in currently decided image-video codingstandard algorithms[8}.

An analysis of band energy distributions in perfect reconstruction (PR) multirate systems is presented, and the effects of aliasing are evaluated. A performance measure called nonaliasing energy ratio (NER) is defined. The merit of the new measure is emphasized with performance comparisons of the popular block transforms and two-band PR-QMF based hierarchical filter banks. It is shown that the new measure complements the widely used energy compaction measure and is consistent with the subjective performance results. >

The wavelet transforms, particularly those for orthonormal and biorthogonal wavelets with finite support, have emerged as a new mathematical tool for multiresolution decomposition of continuous-time signals with potential applications in computer vision, signal coding, and others. These wavelet bases are closely linked with the unitary two-band perfect reconstruction quadrature mirror filter (PR-QMF) banks and biorthogonal filter banks. This chapter describes the continuous wavelet transform as a signal analysis tool with the capability of variable time-frequency localization and compares it with the fixed localization of the short-time Fourier transform. The chapter discusses link between the multiresolution aspect of the compactly supported wavelet and subband techniques. The two-band PR filter banks play a major role in this linkage. The multiresolution aspect becomes clear upon developing the one-to-one correspondence between the coefficients in the fast wavelet transform and the dyadic or octave-band subband tree. The chapter introduces the concept of Haar wavelets, which are discontinuous in time, and the Shannon wavelets, which are discontinuous in frequency, to illustrate the examples of multiresolution wavelet concepts.

A multivariable optimization problem is set to design 2-band PR-QMFs in this paper. The energy compaction, aliasing energy, step response, zero-mean high-pass filter, uncorrelated subband signals, constrained nonlinearity of the phase-response, and the given input statistics are simultaneously considered in the proposed optimal filter design technique. A set of optimal PR-QMF solutions and their optimization criteria along with their energy compaction performance are given for comparison. This approach of PR-QMF design leads to an input driven adaptive subband filter bank structure. It is expected that these optimal filters outperform the well-known fixed PR-QMFs in the literature for image and video coding applications.

This paper describes a class of orthogonal binomial filters which provide a set of basis functions for a bank of perfect reconstruction Finite Impulse Response Quadrature Mirror Filters (FIR-QMF). These Binomial QMFs are shown to be the same filters as those derived from a discrete orthonormal wavelet approach by Daubechies [13]. The proposed filters can be implemented very efficiently with output scaling but otherwise no multiply operations. The cornpaction performance of the proposed signal decomposition technique is computed and shown to be better than that of the DCT for the AR(1) signal models and also for standard test images.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A density evolution procedure for low-density parity-check (LDPC) codes under Max-Log-MAP decoding is presented. Using this technique, the precise convergence threshold for LDPC code could be easily derived.

Perfect reconstruction quadrature mirror filters (PR QMF’s) have been proposed as structures suitable for hierarchical subband coding [ ll-[4], and also for multiresolution signal decomposition as might be used in image pyramid coding [5]. More recently, multiresolution signal decomposition methods are being examined from the standpoint of the discrete wavelet transform for continuous-time signals [6]-[8]. In this paper, we describe a class of orthogonal binomial filters that provide basis functions for a perfect reconstruction bank of finite impulse response QMF’s. The orthonormal wavelet filters derived by Daubechies 171 from a discrete wavelet transform approach are shown to be the same as the solutions inherent in the binomial-based filters. The energy compaction performance of the binomial QMF decomposition is computed and shown to be better than the DCT for the Markov source models, as well as real-world images considered. The proposed binomial structure is efficient, simple to implement on VLSI, and suitable for multiresolution signal decomposition and coding applications.

In many still-image and video processing applications, the time-frequency localization properties of the decomposition technique are an important consideration. While bandwidth compression of the image requires operators with good localization in frequency, spatial features such as edge preservation demand a high degree of localization in time (or the spatial variable). These requirements compete with each other and one is secured at the expense of the other. The classical "uncertainty" principle in the continuous-time domain provides the back drop for this trade-off. Our purpose is to review recent extensions of this principle to the discrete-time case and to develop optimum wave forms. We review common features of block transforms, subband filter banks, and wavelets, and demonstrate how the discrete uncertainty can be used to evaluate these decomposition methods. In particular, we evaluate the trade-off between localization in time and in frequency for several proposed signal decomposition structures.

Well-known block transforms and perfect reconstruction orthonormal filter banks are evaluated based on their frequency behavior and energy compaction. The filter banks outperform the block transforms for the signal sources considered. Although the latter are simpler to implement and already the choice of the existing video coding standards, filter banks with simple algorithms may well become the signal decomposition technique for the next generation video codecs, which require a multiresolution signal representation.

The majority of the existing data hiding schemes are based on the direct-sequence (DS) modulation where a low-power random sequence is embedded into the original cover signal to represent hidden information. In this paper, we investigate linear and nonlinear modulation approaches in digital data hiding. One typical DS modulation algorithm is explored and its optimal oblivious detector is derived. The results expose its poor cover noise suppression as the hiding signature signal always has much lower energy than the cover signal. A simple nonlinear algorithm, called set partitioning, is proposed and its performance is analyzed. Analysis and simulation studies further demonstrate improvements over the existing schemes.

The hybrid coding technique developed here involves a function of two concepts: progressive interactive image transmission coupled with transform differential coding. There are two notable features in this approach. First, a local average of an mxm (typically 5 x 5) pixel array is formed, quantized and transmitted to the receiver for a preliminary display. This initial pass provides a crude but recognizable image before any further processing or encoding. Upon request from the receiver, the technique then switches to an iterative transform differential encoding scheme. Each iteration progressively provides more image detail at the receiver as requested. Secondly, this hybrid coding technique uses a computationally efficient, real, orthogonal transform, called the Modified Hermite Transform(MHT) [1], to encode the difference image. This MHT is then compared with the Discrete Cosine Transform(DCT) [2] for the same hybrid algorithm. For the standard images tested, we found that the progressive differential coding method per-forms comparably to the well-known direct transform coding methods. The DCT was used as the standard in this traditional approach. This hybrid technique was within 5% of SNR peak-to-peak for the "LENA" image. Comparisons between MHT and DCT as the transform vehicle for the hybrid technique were also conducted. For a transform block size N=8, the DCT requires 50% more multiplications than the MHT. The price paid for this efficiency is modest. For the example tested ("LENA"), the DCT performance gain was 4.2 dB while the MHT was 3.8 dB.

The motion compensated frame difference (MCFD) signals of video have much less correlation than the still frames. This change of source statistics brings the compactness power of the 2D DCT down. The Hadamard matrix decomposition 1D DCT of MCFD is proposed in this paper. The 1D DCT case is compared with the 8x8 2D DCT case. The results indicate that both perform the same practically. But the first is simpler computationally than the latter. Transform coefficients split into bands and each band is grouped based on the variances. The VQ of transform coefficient bands is employed. 35.77 dB of PSNR at 0.3245 bits/pixel is obtained with the proposed scheme which is also perceptually as good as the 2D DCT case.