Accurate and non-invasive monitoring of hematopoietic stem cell transplantation (HSCT) patients is crucial but challenging due to factors including the limitations of traditional biopsies and the intensive manual analysis required for emerging comprehensive imaging techniques like FLT PET/CT. Furthermore, low-dose CT resolution in this vulnerable patient population often hinders precise vertebral body segmentation, a critical step for comprehensive marrow compartment assessment. This paper presents a novel approach for per-vertebral body instance segmentation in HSCT FLT PET/CT scans, addressing the inherent difficulties of low-resolution data and limited annotated training cases. Our method leverages an attention-gated U-Net architecture, significantly enhanced by a novel data augmentation strategy involving downsampled high-resolution VerSe dataset images. We demonstrate, for the first time, accurate vertebral body segmentation on this challenging low-resolution dataset. Our approach integrates an attention-based U-Net model and is compared against TotalSegmentator as a baseline, showing superior segmentation performance, particularly in the anatomically complex upper spine where TotalSegmentator exhibits suboptimal results. To the best of our knowledge, this work reports fully automated high-quality instance segmentation results for individual vertebral bodies in CT volumes of HSCT FLT PET/CT patients for the first time, promising to facilitate automation for critical quantitative assessments like SUV measurement and ultimately improve long-term patient management and outcomes.

Learning the latent representation of three-dimensional (3D) morphable geometry is useful for several tasks, such as 3D face tracking, human motion analysis, and character generation and animation. For unstructured surface meshes, previous state-of-the-art methods focus on designing convolution operators and share the same pooling and unpooling operations to encode neighborhood information. Previous models use a mesh pooling operation based on edge contraction, which is based on the euclidean distance of vertices rather than the actual topology. In this study, we investigated whether such a pooling operation can be improved, introducing an improved pooling layer that combines the vertex normals and adjacent faces area. Furthermore, to prevent template overfitting, we increased the receptive field and improved low-resolution projection in the unpooling stage. This increase did not affect processing efficiency because the operation was implemented once on the mesh. We performed experiments to evaluate the proposed method, whose results indicated that the proposed operations outperformed Neural3DMM with 14% lower reconstruction errors and outperformed CoMA by 15% by modifying the pooling and unpooling matrices.

Federated learning is a contemporary machine learning paradigm where locally trained models are distilled into a global model. Due to the intrinsic permutation invariance of neural networks, Probabilistic Federated Neural Matching (PFNM) employs a Bayesian nonparametric framework in the generation process of local neurons, and then creates a linear sum assignment formulation in each alternative optimization iteration. But according to our theoretical analysis, the optimization iteration in PFNM omits global information from existing. In this study, we propose a novel approach that overcomes this flaw by introducing a Kullback-Leibler divergence penalty at each iteration. The effectiveness of our approach is demonstrated by experiments on both image classification and semantic segmentation tasks.

This paper designs a Distributed Arithmetic Coding (DAC) decoder using the depth-first search method. In addition, a method is proposed to control the decoder complexity. Simulation results compare the DFD with the traditional Breadth-First Decoder (BFD) showing that under the same complexity constraints, the DFD outperforms the BFD when the code length is not too long and the quality of side information is not too poor.

Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. This paper proposes high-accuracy classification of gait phases and muscle actions, using readings from low-cost motion capture systems. First, 12 gait parameters, drawn from the medical literature, are defined to characterize gait patterns. These proposed parameters are then used as input to our proposedmulti-channel time-series classificationand gait phase reconstruction methods. Proposed methods fully utilize temporal information of gait parameters, thus improving the final classification accuracy. The validation, conducted using 126 experiments, with 6 healthy volunteers and 9 stroke survivors with manually-labelled gait phases, achieves state-of-art classification accuracy of gait phase with lower computational complexity compared to previous solutions.1

In digital signal processing, a shift-invariant filter can be represented as a polynomial expansion of a shift operation, that is, the Z-transform representation. When extended to graph signal processing (GSP), this would mean that a shift-invariant graph filter can be represented as a polynomial of the shift matrix of the graph. Prior work shows that this holds under the shift-enabled condition that the characteristic and minimum polynomials of the shift matrix are identical. While the shift-enabled condition is often ignored in the literature, this letter shows that this condition is essential for the following reasons. First, we prove that this condition is not just sufficient but also necessary for any shift-invariant filter to be representable by the shift matrix. Moreover, we provide a counterexample showing that given a filter that commutes with a non-shift-enabled graph, it is generally impossible to convert the graph into a shift-enabled graph with a shift matrix still commuting with the original filter. The result provides a deeper understanding of shift-invariant filters when applied in GSP and shows that further investigation of shift-enabled graphs is needed to make them applicable to practical scenarios.

With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy.

Summary form only given. Motivated by the correlation channel modeling problem in practical applications, such as distributed video coding, we study the binary source coding of a uniform source with side information, under asymmetric correlation channel assumptions. First, we consider the case where side information is available to both the encoder and decoder, and give an analytical formula for the rate-distortion bound. Then, we consider the side information to be available only to the decoder and present the derivation of the associated Wyner-Ziv rate-distortion bound. Most importantly, we characterize the evolution of the rate-loss suffered by Wyner-Ziv coding, for all possible binary asymmetric correlation channels.

We present an efficient approach to text localization. We restrict our attention to the text localization and refer to our method as 'text search'. In particular, we pre-trained a text detection ConvNet which skip the region proposal process, and integrates bounding boxes and text confidence score into the output layer. Our approach achieved similar high precision with [1], and acceptable level of recall.

Distributed arithmetic coding (DAC) is an effective technique for implementing Slepian-Wolf coding (SWC). It has been shown that a DAC code partitions source space into unequal-size codebooks, so that the overall performance of DAC codes depends on the cardinality and structure of these codebooks. The problem of DAC codebook cardinality has been solved by the so-called codebook cardinality spectrum (CCS). This paper extends the previous work on CCS by studying the problem of DAC codebook structure. We define Hamming distance spectrum (HDS) to describe DAC codebook structure and propose a mathematical method to calculate the HDS of DAC codes. The theoretical analyses are verified by experimental results.

Distributed arithmetic coding (DAC) is a variant of AC that can realize Slepian-Wolf coding in a nonlinear way. In our previous work, we defined codebook cardinality spectrum (CCS) and Hamming distance spectrum (HDS) for DAC. In this paper, we make use of CCS and HDS to analyze tailed DAC, which is a form of DAC that, as traditional AC, maps the last few symbols of each source block onto non-overlapped intervals. First, we derive the exact HDS formula for tailless DAC, a form of DAC that maps all the symbols of each source block onto overlapped intervals, and show that the HDS formula previously given is in fact approximation. Then, the HDS formula is extended to tailed DAC. Using CCS, we also deduce the average codebook cardinality, which is closely related to decoding complexity, and rate loss of tailed DAC. The effects of tail length are extensively analyzed. It is revealed that by increasing tail length to a value not close to the bitstream length, closely spaced codewords within the same codebook can be removed at the cost of a higher decoding complexity and a larger rate loss. Finally, theoretical analyses are verified by experiments.

We present an in-depth analysis of the problem of lossy compression of binary sources in the presence of correlated side information, where the correlation is given by a generic binary asymmetric channel and the Hamming distance is the distortion metric. Our analysis is motivated by systematic rate-distortion gains observed when applying asymmetric correlation models in Wyner-Ziv video coding. First, we derive for the first time the rate-distortion function for conventional predictive coding in the binary-asymmetric-correlation-channel scenario. Second, we propose a new bound for the case where the side information is only available at the decoder-Wyner-Ziv coding. We conjecture this bound to be tight. We show that the maximum rate needed to encode as well as the maximum rate-loss of Wyner-Ziv coding relative to predictive coding corresponds to uniform sources and symmetric correlations. Importantly, we show that the upper bound on the rate-loss established by Zamir is not tight and that the maximum value is actually significantly lower. Moreover, we prove that the only binary correlation channel that incurs no rate-loss for Wyner-Ziv coding compared with predictive coding is the Z-channel. Finally, we complement our analysis with new compression performance results obtained with our state-of-the-art Wyner-Ziv video coding system.

Super-resolution as an exciting application in image processing was studied widely in the literature. This paper presents new approaches to video super-resolution based on sparse coding and belief propagation. First, find candidate match pixels on multiple frames using sparse coding and belief propagation. Second, incorporate information from these candidate pixels with weights computed using the nonlocal-means method in the first approach or using the sparse coding and belief propagation method in the second approach. The effectiveness of the proposed methods is demonstrated for both synthetic and real video sequences in the experiment section. In addition, the experimental results show that our models are naturally robust in handling super-resolution on video sequences affected by scene motions and/or small camera motions.

Recently, a multiple Slepian-Wolf Coding method called Matrix Partition Codes has been introduced. However, no explicit decoding algorithm has been established so far. In this letter, we propose a two-step decoding algorithm which just needs an extra one-time preprocessing (O(rsn|D|+ r|D|log(|D|) + n2sm)). To illustrate the efficiency of our algorithm, we analyze the time complexity of our algorithm, and the results show that the complexity of our method (O(r log(|D|) + msn)) is exponentially better than the brute-force search method (O(|F|n|D|msn)).

Summary form only given. Distributed Source Coding (DSC) is rapidly gaining popularity, and has many good applications. However, some important correlations are sometimes omitted, such as temporal correlation. In this paper, we consider the correlations of the source data both in spatial and temporal domains for DSC decoding. And this is equally to integrate a Kalman filter in our algorithm. We tested our algorithm on the practical temperature network, and the results turn out it achieved better performance than the algorithm without temporal correlation.

Summary form only given. In this paper, we take a different approach from the coding community. Instead of taking the usual route of quantization plus Slepian-Wolf coding, we do not perform any Slepian-Wolf coding on the transmitter side. We simply perform quantization on the sensor readings, compress the quantization indexes with conventional entropy coding, and send the compressed indexes to the receiver. On the decoder side, we simply perform entropy decoding and Gaussian process regression to reconstruct the joint source. To reduce the sum rate over all sensors, some sensors are censored and do not transmit anything to the decoder.

In this paper, we develop a novel genome compression framework based on distributed source coding (DSC)[3], which is specially tailored to the need of miniaturized devices. At the encoder side, subsequences with adaptive code length can be compressed flexibly through either low complexity DSC based syndrome coding or hash coding with the decision determined by the existence of variations between source and reference known from the decoder feedback. Moreover, to tackle the variations between source and reference at the decoder, we carefully designed a factor graph based low-density parity-check (LDPC) decoder, which automatically detects insertion, deletion and substitution.

Multiple description coding (MDC) has been proposed as a possible solution to real-time video delivery over relay-assisted wireless networks to exploit path diversity. In this paper, we study layered multiple description video over relay-assisted mobile networks, such as LTE-A, and develop a framework that routes packets to different relays based on source-channel-relay parameters. The proposed system comprises, besides layered MDC, (i) application-layer forward error correction via Random linear codes (RLC) that are very suitable in the relaying scenarios, due to their rateless nature, (ii) unequal error protection (UEP), using the recently proposed expanding window technique, that relies on probabilistic scheduling of layered packets. We generate layered multiple descriptions using data slicing and data partitioning features of H.264/AVC and conduct simulations by modelling a two-relay LTE-A setup with the finite state Markov chain model. Simulation results show benefits of relaying with optimized routing and UEP.

In this paper, we use linear codes to study zero-error Slepian–Wolf coding of a set of sources with deviation symmetry, where the sources are generalization of the Hamming sources over an arbitrary field. We extend our previous codes, generalized Hamming codes for multiple sources, to matrix partition codes and use the latter to efficiently compress the target sources. We further show that every perfect or linear-optimal code is a matrix partition code. We also present some conditions when matrix partition codes are perfect and/or linear-optimal. Detail discussions of matrix partition codes on Hamming sources are given at last as examples.

Estimating accurately channel noise has significant impact on the performance of channel coding. In this paper, we present a low complexity online estimator at the decoder side for low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels with a time-varying noise. The proposed algorithm is obtained by modeling the channel statistic as a Bayesian inference problem on a factor graph. Since BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel, we apply the Laplace propagation (LP) algorithm on the proposed algorithm. Through simulations, we show that the proposed algorithm can simultaneously decode LDPC codes and estimate the time-varying SNR at the bit-level, which enhance the BP decoding performance. Moreover, the proposed LP estimator shows a very low computational complexity.

In order to estimate the amount of energy that will be generated by a wind farm and provide efficient power distribution planning, it is necessary to deliver information of wind speed at all wind turbines. This paper proposes a scheme for compressing wind speed measurements exploiting both temporal and spatial correlation between the turbine readings via distributed source coding. The proposed scheme relies on a correlation model based on true measurements. A compression scheme proposed is of low encoding complexity and uses a particle-filtering based belief propagation decoder that adaptively estimates the nonstationary noise of the correlation model. Simulation results using realistic models show significant performance improvements compared to the scheme that does not dynamically refine correlation.

Distributed video coding (DVC) is rapidly gaining popularity as a low cost, robust video coding solution, that reduces video encoding complexity. DVC is built on distributed source coding (DSC) principles where correlation between sources to be compressed is exploited at the decoder side. In the case of DVC, a current frame available only at the encoder is estimated at the decoder with side information generated from other frames available at the decoder. One of the main challenges in DVC design is that correlation among the source and side information needs to be estimated online and as accurately as possible. Since correlation dynamically changes with the scene, in order to exploit the robustness of DSC code designs, we integrate particle filtering (PF) with standard belief propagation (BP) decoding for inference on one joint factor graph to estimate correlation among source and side information. Correlation estimation is performed online as it is carried out jointly with decoding of the graph-based DSC code. Moreover, we demonstrate our joint bit-plane decoding with adaptive correlation estimation schemes within state-of-the-art DVC systems, which are transform-domain based with a feedback channel for rate adaptation. Experimental results show that our proposed system gives a significant performance improvement compared to the benchmark state-of-the-art DISCOVER codec (including correlation estimation) and the case without dynamic PF tracking, due to improved knowledge of timely correlation statistics via the combination of joint bit-plane decoding and particle-based BP (PBP) tracking.

We propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's Solar TErrestrial RElations Observatory (STEREO) project. Our experimental results show improved compression performance w.r.t. to a benchmark compression scheme, accurate correlation estimation by our proposed particle-based belief propagation algorithm, and significant peak signal-to-noise ratio improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation.

In this paper, we propose a system using video cameras to perform vehicle identification. We tackle this problem by reconstructing an input by using multiple linear regression models and compressed sensing, which provide new ways to deal with three crucial issues in vehicle identification, namely, feature extraction, online vehicle identification database buildup , and robustness to occlusions and misalignment. The results show the capability of the proposed approach.

Belief propagation (BP) is a powerful algorithm to decode low- density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates expectation propagation (EP) into the BP based LDPC decoding process. The proposed scheme is able to perform online estimation of both stationary and time-varying SNR at the bit-level, and enhance the BP decoding performance simultaneously. Moreover, the proposed EP estimator shows a very fast convergence speed, and the additional computational overhead of the proposed decoder is less than 10% of the standard BP decoder.

In this paper, we propose an adaptive Distributed Video Coding (DVC) scheme that dynamically estimates correlation statistics of the scene in a video sequence to enhance belief-propagation (BP) Slepian-Wolf (SW) decoding. In order to exploit the robustness of distributed source coding (DSC) designs, we integrate particle filtering with standard BP decoding in one factor graph to estimate online correlation among source and side information. Our proposed system boasts improved performance over classical DVC without correlation estimation, due to improved knowledge of correlation statistics via the combination of bit-plane coding and particle-based BP tracking in each frame, as shown by our results.

The paper presents a scalable compressive sampling (CS) scheme for video acquisition. The proposed solution enables progressive reconstruction of video frames with novel measurement matrices. Simulation results show significant performance improvements over the traditional CS technique for the base layer and slightly better performance for the final enhancement layer.

Distributed compression or Distributed Source Coding (DSC) refers to separate compression and joint decompression of multiple correlated sources. Though theoretical foundations were set almost forty years ago, driven by applications such as wireless video surveillance and wireless multimedia communications, DSC has become and still is a very active research area with interest from both academia and industry. While the first decade of this century has seen massive progress in code designs and achievable bounds, recent work is focussed on applications and solving practical DSC limitations such as large codeword size, accurate correlation estimation, etc. The goal of this paper is to provide an overview of the exciting trends and novel applications such as security, remote sensing, wireless data gathering of correlated data, biomedical imaging and infotainment, that DSC makes possible.

Acquiring and processing astronomical images is becoming increasingly important for accurate space weather prediction and expanding our understanding about the Sun and the Universe. These images are often rich in content, large in size and dynamic range. Efficient, low-complexity compression solutions are essential to reduce onboard storage, processing, and communication resources. Distributed compression is a promising technique for onboard coding of solar images by exploiting correlation between successively acquired images. In this paper we propose an adaptive distributed compression solution using particle filtering that tracks correlation, as well as performing disparity estimation, at the decoder side. The proposed algorithm is tested on the stereo solar images captured by the twin satellites system of NASA's STEREO project. Our experimental results show the significant PSNR improvement over traditional separate bit-plane decoding without dynamic correlation and disparity estimation.

This study considers a multi-class network resource in a competitive market where each user endowed with an initial budget will purchase bandwidth from each class of the network resource to maximise its utility function. The authors use revenue as the utility function for the service provider, and enhance Kelly's utility function by including delay as the quality of service (QoS) parameter for users. A competitive equilibrium is reached when the total bandwidth is allocated, each user spends all its budget and the utility functions are independently and simultaneously maximised. The authors prove that such equilibriums always exist and, under fixed bandwidth supply for each class of service, the equilibrium is also unique. Furthermore, the authors discuss how to adjust the initial endowment of each user to meet his or her individual bandwidth constraint, either from constraint on the access network or the limitation of the user equipment. Under this bandwidth constraint condition, the proposed competitive equilibrium yields the price for each class of service, budget redistribution and bandwidth allocation among all users. The competitive market model presented provides a solution for pricing a multi-class network resource and allocating the resource that achieves both higher social utility and better individual satisfaction whereas maintaining the QoS of each class.

A major difficulty that plagues the practical use of Slepian-Wolf (SW) coding (and distributed source coding in general) is that the precise correlation among sources needs to be known a priori. To resolve this problem, we propose an adaptive asymmetric SW decoding scheme using particle based belief propagation (PBP). We explain the adaptive scheme for asymmetric setup in detail and then further extend it to the non-asymmetric setup based on the code partitioning approach. Moreover, we introduce a Metropolis-Hastings (MH) algorithm in the resampling step, which efficiently decreases the number of simulation iterations. We show through experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the joint correlation among sources. Further, comparing to the conventional SW decoder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics.

We consider zero-error Slepian-Wolf coding for a special kind of correlated sources known as Hamming sources. Moreover, we focus on the design of codes with minimum redundancy (i.e., perfect codes). As shown in a prior work by Koulgi et al., the design of a perfect code for a general source is very difficult and in fact is NP-hard. In our recent work, we introduce a subset of perfect codes for Hamming sources known as Hamming Codes for Multiple Sources (HCMSs). In this work, we extend HCMSs to generalized HCMSs, which can be proved to include all perfect codes for Hamming sources. To prove our main result, we first show that any perfect code for a Hamming source with two terminals is equivalent to a Hamming code for asymmetric Slepian Wolf coding (c.f. Lemma 2). We then show that any multi-terminal (of more than two terminals) perfect code can be transformed to a perfect code for two terminals (c.f. Lemma 3) and to a perfect code with an asymmetric form (c.f. Lemma 4). Equipped with these results, we prove that every perfect Slepian-Wolf code for Hamming sources is equivalent to a generalized HCMS.

Belief propagation (BP) is a powerful algorithm to decode low-density parity check (LDPC) codes over additive white Gaussian noise (AWGN) channels. However, the traditional BP algorithm cannot adapt efficiently to the statistical change of SNR in an AWGN channel. This paper proposes an adaptive scheme that incorporates a particle filtering (PF) algorithm into the BP based LDPC decoding process. The proposed scheme is capable to perform online estimation of time-varying SNR at the bit-level and enhance the BP decoding performance simultaneously.

We consider Slepian-Wolf (SW) coding of multiple sources and extend the packing bound and the notion of perfect code from conventional channel coding to SW coding with more than two sources. Moreover, we show that there does not exist perfect SW code of length-5 for three sources.

This paper addresses the problem of distributed source coding (DSC) of binary sources with unknown varying correlation statistics over erasure channels. We propose an adaptive asymmetric Slepian-Wolf (SW) decoding scheme using particle-based belief propagation (BP) based on Raptor codes. We show through the experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the correlation between source and side information. Moreover, compared to the conventional Raptor decoder, the proposed approach can achieve a higher compression ratio and provide stronger erasure protection under unknown varying correlation statistics. The ability to estimate the statistical correlation in the code structure makes our approach very useful for real applications.

In this paper we propose an adaptive Wyner-Ziv (WZ) coding scheme for non-binary correlated sources with side information at the decoder. At the encoder, a low density parity check (LDPC) code is employed to implement Slepian-Wolf (SW) coding for each bit-plane independently. The joint "source-channel" decoder, which combines SW decoding and dequantizing in a single step, preserves the symbol-domain correlation and decreases the distortion. Moreover, the incorporation of particle-based belief propagation (BP) in the decoder can extend the scheme to perform an online estimation of the correlation and enhance the BP decoding performance simultaneously. Our proposed framework is universal and can be applied to any parametric correlation model.

A major difficulty that plagues the practical use of Slepian-Wolf coding (and distributed source coding in general) is that the precise correlation among sources need to be known a priori. To resolve this problem, we have proposed an adaptive asymmetric Slepian-Wolf decoding scheme using particle filtering based belief propagation in our recent work. In this paper, we extend the adaptive scheme to the non-asymmetric setup based on the code partitioning approach. We show through experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the joint correlation among sources. Further, comparing to the conventional Slepian-Wolf decoder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics.

In this paper, we propose an interactive segmentation method to apply user information during the segmentation of a specific anatomic structure. This method is formulated to use belief propagation to minimize a global cost function according to local level sets. The propagation starts with one user labeled point, and iteratively extends the user information from the labeled pixel to its neighborhood by calculating the beliefs of the pixels in the same level as the labeled pixel. Since the segmentation relies on both local user information and global image features, it is less interrupted by noise, and works well even the target is not obvious to its neighbor. The promising segmentation results also show that our method is robust to the objects with high shape variation and inhomogeneous intensity value appearance.

The popular Richardson-Lucy (RL) image deconvolution algorithm often produces undesirable ringing artifacts. In this paper, we propose a novel Dual Range Deringing (DRD) algorithm to address this problem. As a post-deconvolution scheme, the proposed approach follows RL deconvolution and removes ringing artifacts by utilizing information from both the input blurred image and the RL-deblurred image. DRD first marks smooth regions in the input blurred image that are likely to be subjected to ringing artifacts far away from any strong edge. It then identifies short-range ringing artifacts from the regions that surround strong edges in the RL-deblurred image. Once marked, both long- and short-range ringing artifacts are then suppressed by an edge-preserving deringing filter. We demonstrate the effectiveness of this procedure by performing experiments on a set of images blurred with various Point Spread Functions (PSFs). We compare DRD with state-of-the-art non-blind deconvolution algorithms and show that our results are virtually free of ringing artifacts with only minor detail losses. Moreover, DRD consists of computationally efficient local operations and is suitable for parallelization on modern GPUs.

We introduce Hamming Codes for Multiple Sources (HCMSs) as a potential solution of perfect Slepian-Wolf (SW) coding for arbitrary number of terminals. Moreover, we study the case with three sources in detail. We present the necessary conditions of a perfect SW code and show that there exists infinite number of HCMSs. Moreover, we show that for perfect SW code with sufficiently long code length, the compression rates of different sources can be trade-off flexibly.

Scale Invariant Feature Transform (SIFT) is a very powerful technique for image registration. While SIFT descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approach for registration is overly simplified, because it completely ignores the geometric information among descriptors. In this paper, we formulate keypoint matching as a global optimization problem and provide a suboptimum solution using belief propagation. Experimental results show significant improvement over previous approaches.

This paper addresses the problem of wireless delivery of a captured scene from two cameras, which do not communicate with each other, to a central point for joint decoding. We exploit correlation among two camera views using distributed source coding and use complete complementary (CC) data spreading to combat multiple access interference and noise even when the transmitters are de-synchronized. Our distributed source coding scheme is based on uniform scalar quantization in the DCT domain and non-asymmetric Slepian-Wolf coding via turbo codes. The non-asymmetric Slepian-Wolf scheme enables efficient trade-off between the transmission rates of the two cameras. Simulation results indicate that the proposed system outperforms significantly two independently JPEG-encoded streams at low transmission rates.

This paper describes an optical system for low-complexity optical image acquisition based on a single scanning MEMS mirror and a single photodetector. The overall aim of the research is to investigate techniques for image acquisition at electromagnetic wavelengths where the cost and/or technical maturity of detector arrays pose a limitation. In contrast to similar systems built using a digital micromirror device (DMD), the present configuration has advantages of lower cost and potential applicability across a wide spectrum, ranging from visible to Terahertz frequencies. In the present arrangement, light at visible wavelengths from the object passes through a telescope and falls onto a small, scanning MEMS micromirror. The entire image of the object is projected onto the mirror surface and reflected towards a single photodetector with a pinhole at its entrance. Similarly to conventional scanning, by finely changing the tilt-angle of the mirror, the detector sees different areas of the projected image, thereby building up an image pixel-by-pixel. Resolution is increased by allowing for an overlap between neighbouring scanned areas. Iterative bilinear interpolation and wavelet denoising are employed to enhance image quality.

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.

The paper combines distributed source coding and compressive sampling for efficient spectrum estimation. Two or more cognitive radios sample the spectrum compressively and independently compress their observations using multiterminal source coding. A central hub collects compressed streams from these radios before performing joint multiterminal source decoding followed by iterative signal reconstruction. Simulation results are provided for two radios performing practical multiterminal source coding with uniform scalar quantization and systematic turbo codes for Slepian-Wolf coding and error protection.

This paper considers trellis coded quantization (TCQ) and low-density parity-check (LDPC) codes for the quadratic Gaussian Wyner-Ziv coding problem. After TCQ of the source X, LDPC codes are used to implement Slepian-Wolf coding of the quantized source Q(X) with side information Y at the decoder. Assuming 256-state TCQ and ideal Slepian-Wolf coding in the sense of achieving the theoretical limit H(Q(X)|Y ), we experimentally show that Slepian-Wolf coded TCQ performs 0.2 dB away from the Wyner-Ziv distortion-rate function DWZ(R) at high rate. This result mirrors that of entropy-constrained TCQ in classic source coding of Gaussian sources. Furthermore, using 8,192-state TCQ and assuming ideal Slepian-Wolf coding, our simulations show that Slepian-Wolf coded TCQ performs only 0.1 dB away from DWZ(R) at high rate. These results establish the practical performance limit of Slepian-Wolf coded TCQ for quadratic Gaussian Wyner-Ziv coding. Practical designs give performance very close to the theoretical limit. For example, with 8,192-state TCQ, irregular LDPC codes for Slepian-Wolf coding and optimal non-linear estimation at the decoder, our performance gap to DWZ(R) is 0.20 dB, 0.22 dB, 0.30 dB, and 0.93 dB at 3.83 bit per sample (b/s), 1.83 b/s, 1.53 b/s, and 1.05 b/s, respectively. When 256-state 4-D trellis-coded vector quantization instead of TCQ is employed, the performance gap to DWZ(R) is 0.51 dB, 0.51 dB, 0.54 dB, and 0.80 dB at 2.04 b/s, 1.38 b/s, 1.0 b/s, and 0.5 b/s, respectively.

Latest advances in sensor and communication technologies and computational intelligence will allow vehicles to observe their surroundings, process the collected information and communicate it with other vehicles, e.g., vehicles can sense adverse road conditions, experience trafic congestion, or detect accidents, and then adjust vehicle control. We investigate the problem of effective intelligence gathering in the perspectives of both communications and data fusion. The target system should have high processing and transmission accuracy for the gathered information as well as low communication load requirement. Assuming a binary source, we determine the theoretical bound for the error probability of gathered information and present a way of communicating the observation from individual vehicles to the destination. Compared to the flooding method, our model reduces the communication load by approximately N/log2N times, with N being the total number of the vehicles, without decreasing the accuracy of the gathered information.

Driven by applications in data-hiding, MIMO broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source- channel coding for algebraic binning. We then address practical code design based on nested lattice codes and propose nested turbo codes using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to TCQ, turbo-like TCQ offers structural similarity between the source and channel coding components, leading to more efficient nesting with TTCM and better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. Optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions and exhibits a gap of 0.94, 1.42 and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 bits/sample, respectively.

In this paper, a novel scheme is introduced for human facial feature extraction. Unlike previous methods that fit a 3D morphable model to 2D intensity images, our scheme utilizes 3D range images to extract features without requiring manually-defined initial landmark points. A linear transformation is used to achieve the mapping between the 3D model and a 3D range image, which makes the computation simple and fast. Moreover, our scheme is robust to the illumination and pose variations. In addition to features from range images, extra features can be obtained by examining optional 2D texture images. Using our scheme, we can also perform automatic eye/mouth corner localization. Experimental results show the high accuracy and robustness of our scheme.

We consider a practical system design for a wireless video conference application, where we exploit the broadcast nature of wireless radio links using network coding. With network coding, the number of necessary downlink transmissions from the intermediate node to the two users is reduced, and thus the throughput is increased. We develop two systems, one based on amplify-and-forward and another on decode-and-forward technique, and compare them to traditional communication systems that do not use network coding. Our practical designs exploit the latest in 3-D wavelet-based scalable video coding and channel coding. Simulation results confirm the advantages of the proposed video communication schemes over conventional ones.

Driven by the applications in data hiding, multiple-input multiple-output broadcast channel coding, precoding for interference cancellation, and transmitter cooperation in wireless networks, Costa coding has lately become a very active research area. In this paper, we first offer code design guidelines in terms of source channel coding for algebraic binning. We then address a practical code design based on nested lattice codes, and propose nested turbo codes by using turbo-like trellis-coded quantization (TCQ) for source coding and turbo trellis-coded modulation (TTCM) for channel coding. Compared to the TCQ, the turbo-like TCQ offers a structural similarity between the source and channel coding components, leading to a more efficient nesting with TTCM and a better source coding performance. Due to the difference in effective dimensionality between turbo-like TCQ and TTCM, there is a performance tradeoff between these two components when they are nested together, meaning that the performance of the turbo-like TCQ worsens as the TTCM code becomes stronger and vice versa. The optimization of this performance tradeoff leads to our code design that outperforms existing TCQ/TCM and TCQ/TTCM constructions, and exhibits a gap of 0.94, 1.42, and 2.65 dB to the Costa capacity at 2.0, 1.0, and 0.5 b/s, respectively.

In this paper, we describe a novel 3-D face recognition scheme for 3-D face recognition that can automatically identify faces from range images, and is insensitive to holes, facial expression, and hair. In our scheme, a number of carefully selected range images constitute a set of example faces, and another range image is chosen as a ldquogeneric face.rdquo The generic face is then warped to match each of the example faces in the least mean square sense. Each such warp is specified by a vector of displacement values. In feature extraction operation, when a target face image comes in, the generic face is warped to match it. The geometric transformation used in the warping is a linear combination of the example face warping vectors. The coefficients in the linear combination are adjusted to minimize the root mean square error. After the matching process is complete, the coefficients of the composite warp are used as features and passed to a Mahalanobis-distance-based classifier for face recognition. Our technique is tested on a data set containing more than 600 range images. Experimental results in the access-control scenario show the effectiveness of the extracted features.

Biomedical specimens thicker than the microscope depth-of-field (DOF) produces images of limited quality where a part of the specimen is in focus and others out of focus. Even the best in-focus image always contains objects that are blurred. These blurred contents make the quantitative and accurate analysis of the specimen difficult. The problem can be remedied by extending the DOF of the imaging system. One way to achieve this is to apply digital image fusion, a software technique that combines multiple images into a carefully synthesized image. Another way, albeit more expensive, is to incorporate modification in the optical system. In this paper we describe an extended DOF method that is based on multiscale pointwise product (MPP) image fusion, and compare it with the state-of-the-art wavefront coding technique which involves optical modification. We show competitive performance of our method and contrast its pros and cons with those of the wavefront coding.

Nested lattice quantization provides a practical scheme for Wyner-Ziv coding. This paper examines the high-rate performance of nested lattice quantizers and gives the theoretical performance for general continuous sources. In the quadratic Gaussian case, as the rate increases, we observe an increasing gap between the performance of finite-dimensional nested lattice quantizers and the Wyner-Ziv distortion-rate function. We argue that this is because the boundary gain decreases as the rate of the nested lattice quantizers increases. To increase the boundary gain and ultimately boost the overall performance, a new practical Wyner-Ziv coding scheme called Slepian-Wolf coded nested lattice quantization (SWC-NQ) is proposed, where Slepian-Wolf coding is applied to the quantization indices of the source for the purpose of compression with side information at the decoder. Theoretical analysis shows that for the quadratic Gaussian case and at high rate, SWC-NQ performs the same as conventional entropy-coded lattice quantization with the side information available at both the encoder and the decoder. Furthermore, a nonlinear minimum mean-square error (MSE) estimator is introduced at the decoder, which is theoretically proven to degenerate to the linear minimum MSE estimator at high rate and experimentally shown to outperform the linear estimator at low rate. Practical designs of one- and two-dimensional nested lattice quantizers together with multilevel low-density parity-check (LDPC) codes for Slepian-Wolf coding give performance close to the theoretical limits of SWC-NQ

It has been shown recently that under certain conditions there exist dualities between different multiterminal (MT) source and channel coding problems. Following these results, we study lossless MT source coding and deterministic MT channel coding problems and point out different dualities between them. In particular, we show that there exists a functional duality between a Slepian-Wolf (SW) coding problem and a deterministic broadcast channel (DBC) coding problem and between a lossless multiple-description (MD) coding problem and a deterministic multiple-access channel (DMAC) coding problem. In analogy to the duality established between DBC and DMAC coding problems, we further propose a similar duality between SW and lossless MD coding problems; in this way, we form a closed "duality loop" of four MT coding problems, which imposes the existence of a single common rate point in the achievable rate regions of all four dual problems. We also consider duality in zero-error MT coding and shed light on practical code design with an example. Finally, extension to the case with only one lossless/deterministic component in the source/channel coding problem is provided.

Two novel non-ubiquitous digital watermarking schemes are proposed and evaluated in parallel (one in the spatial domain and one in the wavelet domain) for the purpose of record indexing and integrity protection of medical images. A fingerprint of the regions of interest (ROI) in a medical image is generated using the MD5 message-digest algorithm [R. Rivest, April 1992], and it is embedded along with the medical record information into the non-ROI parts of the image. Our experiments show that with both schemes, a payload of 420 bits of information can be achieved watermarking clinical cytogenetic images, without affecting image content in the ROI nor incurring perceptible differences between original and watermarked images. Furthermore, the resulting watermarked images can survive common image processing operations such as cropping, JPEG compression, sharpening, and their combinations.

In this correspondence, we present iterative algorithms that numerically compute the capacity-power and rate-distortion functions for coding with two-sided state information. Numerical examples are provided to demonstrate efficiency of our algorithms.

Xiong and Malvar recently introduced a nonuniform modulated complex lapped transform (NMCLT) with good time-localization and controllable frequency resolution by using an oversampled nonuniform filter bank to generate its real and the imaginary components. In this paper, we first show that oversampling in the NMCLT is not necessary in theory but a by-product of fast implementation in practice. We also point out that the amount of oversampling, which can be flexibly controlled, depends on the application. We then describe in detail the implementation of the inverse transform, which was not addressed clearly by Xiong and Malvar. We present the first applications of the NMCLT to audio coding and image denoising. A scalable audio coder has been implemented by controlling the amount of oversampling and exploiting redundancy among the NMCLT coefficients via predictive coding. Experimental results show that the audio coder reduces pre-echoes and improves the sound quality of audio clips with transient sounds. A simple denoising algorithm based on the NMCLT has also been devised to provide images with better visual quality than those obtained with wavelet-based soft thresholding.

This paper focuses on successive refinement for the Wyner-Ziv problem and layered code refinement coding scheme for the Wyner-Ziv problem consists of multistage encoders and decoders where each decoder uses all the information generated from previous encoding stages and the side information, which could be different from stage to stage. On extending successive refinability from jointly Gaussian source to the more general type of sources, the difference between the source and the side information is Gaussian and independent of the side information. A layered (successive) coding scheme using nested scalar quantization and Slepian-Wolf coding of bit planes based on LDPC codes is also described.

This paper examines the high-rate performance of low-dimensional nested lattice quantizers for the quadratic Gaussian Wyner-Ziv problem, using a pair of nested lattices with the same dimensionality. As the rate increases, the gap increases between the performances of low dimensional nested lattice quantizers and the Wyner-Ziv rate-distortion function. This gap is due to the relatively weak channel coding component (or coarse lattice) in the nested lattice pair. To enhance the lattice channel code and boost the overall performance, Slepian-Wolf coding is applied to the quantization indices to achieve further compression. Thereby a Wyner-Ziv coding paradigm is introduced using Slepian-Wolf coded nested lattice quantization (SWC-NQ). Theoretical analysis and simulation results show that, for the quadratic Gaussian source and at high rate, SWC-NQ performs the same as traditional entropy-constrained lattice quantization with side information available at both the encoder and decoder.

We consider Slepian-Wolf code design (or source coding with side information) based on LDPC codes. We show that density evolution defined in conventional channel coding can be used in analyzing the Slepian-Wolf coding performance provided that a certain symmetry condition, dubbed dual symmetry, is satisfied by the hypothetical channel between the source and the side information. Exploiting such an analysis. we design an efficient LDPC code based Slepian-Wolf coding scheme and apply it to the quadratic Gaussian Wyner-Ziv problems.

This paper addresses practical code design problems for multiterminal communication networks. The basic element of a multiterminal network code is the binning scheme. We aim to develop a unified practical code design paradigm for related problems in multiterminal networks based on source-channel coding for algebraic binning. First, a framework based on Slepian-Wolf coded quantization is highlighted for Wyner-Ziv coding and multiterminal source coding (e.g., the CEO problem). Then, a nested turbo scheme is proposed for Costa coding by exploiting the duality between Costa coding and Wyner-Ziv coding.

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.

A novel AAC watermarking scheme with low structural and computational complexity is proposed to achieve real-time AAC audio watermark embedding capability. In this scheme, an enhanced spread spectrum watermarking method that increases the robustness and information hiding capacity of the watermarking channel is developed and employed. Furthermore, this scheme improves the speed of the encoding process in that we directly use the quantized indices rather than the dequantized coefficients, thus no explicit dequantization and requantization are performed. Our embedded watermark is tested to be robust against AAC/MP3 transcoding at a rate of 10 bps. The scheme is suitable for speedy encoding and decoding of watermark with low cost hardware.

We study the problem of packetizing embedded multimedia bitstreams to improve the error resilience of source (compression) codes. This problem is important because of the increasing popularity of embedded compression methodology and its suitability for scalable streaming media over IP or/and mobile IP. We study various packetization schemes against packet erasure at both low and high bit rates. Maximizing packetization efficiency for embedded bitstreams is formulated as a discrete optimization problem and globally optimal packetization (OP) algorithms are proposed under different settings. Suboptimal packetization algorithms are also devised to reduce the complexity of the OP algorithms. In order to assess their effectiveness, the proposed packetization algorithms are used to packetize embedded image and video bitstreams with simulated packet loss. Experimental results show that our OP algorithms slightly outperforms suboptimal ones. In addition to confirming the superiority of the OP algorithms, these results also provide justification of heuristic packetization methods published in the literature.

We examine color quantization of images using trellis coded quantization (TCQ). Together with a simple dithering scheme, an 8-bit trellis coded color quantizer reproduces images that are visually indistinguishable from the 24-bit originals. The proposed algorithm can be viewed as a predictive trellis coded color quantization scheme. It is universal in the sense that no training or lookup table is needed. The complexity of TCQ is linear with respect to image size, making trellis coded color quantization suitable for interactive graphics and a window-based display environment.