In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures automatically. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines and nonlinear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (dice similarity index >0.75) in 87-91% of the images. This research aims to enable accurate, consistent, and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female POP.

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.

This paper describes a networked three-dimensional (3-D) rendering framework that allows low-powered mobile devices to simulate the interactive rendering of remote dense 3-D environment. The framework is based on 3-D image warping, especially suited to low-resolution screens and involves the transmission of sparse residual images. The use of an oversized reference image, superview, to cope with the visibility gap errors and the acceleration techniques aimed at improving the frame-rates are explored. It is shown that the described framework offers a potential alternative for interactive rendering of remote 3-D environment on low-powered clients where the triangle mesh display is impractical.

Three-dimensional (3-D) models are information-rich and provide compelling visualization effects. However downloading and viewing 3-D scenes over the network may be excessive. In addition low-end devices typically have insufficient power and/or memory to render the scene interactively in real-time. Alternatively,3-D image warping, an image-based-rendering technique that renders a two-dimensional(2-D) depth view to form new views intended from different viewpoints and/or orientations, may be employed on a limited device. In a networked 3-D environment,the warped views may be further compensated by the graphically rendered views and transmitted to clients at times. Depth views can be considered as a compact model of 3-D scenes enabling the remote rendering of complex 3-D environment on relatively low-end devices. The major overhead of the 3-D image warping environment is the transmission of the depth views of the initial and subsequent references. This paper addresses the issue by presenting an effective remote rendering environment based on the deep compression of depth views utilizing the context statistics structure present in depth views. The warped image quality is also explored by reducing the resolution of the depth map. It is shown that proposed deep compression of the remote rendered view significantly outperforms the JPEG2000 and enables the realtime rendering of remote 3-D scene while the degradation of warped image quality is visually imperceptible for the benchmark scenes.

The IEEE 802.11 working group is currently working on a new standard called IEEE 802.11e to support quality of service (QoS) in WLANs. 802.11e introduces a so-called hybrid coordination function (HCF) containing two medium access mechanisms: enhanced distributed channel access (EDCA) and HCF controlled channel access (HCCA). In the EDCA mechanism, many QoS parameters are introduced including minimum contention window (CWmin), maximum contention window (CWmax), arbitration inter frame space (AIFS) and transmission opportunity limit (TXOPlimit). In this paper, we experimentally assess the MPEG-4 video streaming performance over 802.11e. In particular, we discuss in detail how the human satisfaction of streaming video is affected by the main QoS parameters in IEEE 802.11e WLANs. We measure the level of end user satisfaction together with the network performance and give recommendations regarding the network design and the parameter settings.

The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.

In this paper, a wavelet-based multiscale linear minimum mean square-error estimation (LMMSE) scheme for image denoising is proposed, and the determination of the optimal wavelet basis with respect to the proposed scheme is also discussed. The overcomplete wavelet expansion (OWE), which is more effective than the orthogonal wavelet transform (OWT) in noise reduction, is used. To explore the strong interscale dependencies of OWE, we combine the pixels at the same spatial location across scales as a vector and apply LMMSE to the vector. Compared with the LMMSE within each scale, the interscale model exploits the dependency information distributed at adjacent scales. The performance of the proposed scheme is dependent on the selection of the wavelet bases. Two criteria, the signal information extraction criterion and the distribution error criterion, are proposed to measure the denoising performance. The optimal wavelet that achieves the best tradeoff between the two criteria can be determined from a library of wavelet bases. To estimate the wavelet coefficient statistics precisely and adaptively, we classify the wavelet coefficients into different clusters by context modeling, which exploits the wavelet intrascale dependency and yields a local discrimination of images. Experiments show that the proposed scheme outperforms some existing denoising methods.

In this paper, a very low bitrate video coding scheme using the generalized finite automata (GFA) representation of the bitplane model of video sequences is proposed. Unlike the traditional motion estimate based MPEG scheme, where a group of pictures (GOP) in a video sequence is arranged into I, P and B frames, in the proposed scheme, all the frames in a GOP are first transformed into the wavelet coefficients and the coefficients in subbands are then modeled by the significant bitplanes. Finally, the bitplane model is represented by the genealogy tree structured GFA. The state indices and their transitions in the GFA representation are then entropy coded using the arithmetic code. Since the fractal similarities between the frames, wavelet levels and bit-planes are optimally explored by the GFA, the GFA model of the GOP is minimized, leading to a super low bitrate video coding scheme which can facilitate applications such as video streaming over low bandwidth networks, video streaming to mobile clients (PDA and cellular telephones), mobile-conferencing, video gaming, etc

This correspondence proposes a wavelet-based fractional Brownian motion (fBm) signal estimation scheme. Despite the fact that wavelet transform approximately whitens the fBm processes, it is observed that statistical dependencies still exist across adjacent wavelet scales and between neighboring wavelet coefficients. These dependencies can be exploited to improve the estimation of fBm signals embedded into noise. The idea is to reorganize the wavelet coefficients into a scale-time mixture model and then carry out the minimum mean-square-error estimation (MMSE) using the model. Experiments show that the proposed scheme obtains better estimates than Wornell and Oppenheim's algorithm, in which the wavelet dependencies are not utilized.

Three techniques for increasing the frame rates of three-dimensional warping, namely subsampling, wavelet baseband, and wavelet transform (including high frequencies) are presented. Additionally noninteger and integer splats are applied for the techniques. The Haar baseband and subsampling methods using integer splats both produce excellent frame rates and perceptual quality of warped images.

Depth images, comprising pixel intensities and depth map, are viewed as a compact model of 3D scenes and used in 3D image warping for the distributed or collaborative virtual environment to enable the distributed rendering of complex 3D scenes at relatively low cost. The major overhead of the model is the transmission of the depth images of the initial reference and the subsequent new views that may be up to a few Mbs in size depending on the screen resolution. This paper presents efficient compression techniques specifically designed for the depth images. Additionally we experiment with the warped image quality by reducing the resolution of the depth map. We show that significant compression can be achieved at a cost of a very modest impairment in the perceptual quality of the warped image.

The paper presents a wavelet-based linear minimum mean square-error estimation (LMMSE) scheme to exploit the strong wavelet interscale dependencies for image denoising. Using overcomplete wavelet expansion (OWE), we group the wavelet coefficients with the same spatial orientation at adjacent scales as a vector. The LMMSE algorithm is then applied to the vector variable. This scheme exploits the correlation information of wavelet scales to improve noise removal. To calculate the statistics of wavelet coefficients more adaptively, we classify them into different clusters by the context modeling technique, which yields a good local discrimination between edge structures and backgrounds. Experiments show that the proposed scheme outperforms some existing denoising methods. A biorthogonal wavelet, which well characterizes the interscale dependencies, is found very suitable for the scheme.

This paper presents a spatial-correlation thresholding scheme for noise reduction by wavelet transform. Observing that edge structures are of high magnitude across wavelet scales but noise decays rapidly, we multiply two adjacent wavelet scales to form a spatial-correlation function to enhance significant structures and dilute noise. Dissimilar to the traditional thresholding schemes that apply threshold to the wavelet coefficients, the proposed scheme applies threshold directly to the scale correlation. A robust threshold is presented and experiments show that the proposed scheme outperforms the traditional thresholding methods.

Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.

We propose a novel scheme for image retrieval using a wavelet based multi-scale edge model. All images in the database are decomposed into their multi-scale primal sketch and the background images respectively. The images are stored in the form of the extracted edge structures and background. The similarities between query image and the images in the database are measured based on the statistics of edges structures. The multi-scale edge modeling of image database can also be performed real-time to enable the image retrieval on arbitrary image databases. Experiment shows that the proposed scheme gives promising retrieval performance over the conventional retrieval methods.

This paper presents a new view synthesis technique using the 2D discrete wavelet-based view morphing. View morphing is completely based on pairwise images without camera calibration and depth information of images. First, the fundamental matrix related to any pair of images is estimated. Then, with the fundamental matrix, the pair of image planes is rectified to be parallel and to have their corresponding points lie on the same scanline, which gives an opportunity to generate new views with linear interpolating techniques. The pre-warped images are then decomposed into a hierarchical structure with the wavelet transform. Corresponding coefficients between two decomposed images are therefore linearly interpolated to form the multiresolution representation of an intermediate view. Quantization techniques can be embedded here to compress the coefficients to reduce the morphing complexity. Finally, when displaying, compressed images are decoded and inverse wavelet transformed. A post-warping procedure is employed to transform the interpolated views to its desired position.

In this paper, we study high-fidelity image compression with a given tight L(infinity) bound. We propose some practical adaptive context modeling techniques to correct prediction biases caused by quantizing prediction residues, a problem common to the existing DPCM-type predictive near-lossless image coders. By incorporating the proposed techniques into the near-lossless version of CALIC that is considered by many as the state-of-the-art algorithm, we were able to increase its PSNR by 1 dB or more and/or reduce its bit rate by 10% or more, more encouragingly, at bit rates around 1.25 bpp or higher, our method obtained competitive PSNR results against the best L(2)-based wavelet coders, while obtaining much smaller L(infinity) bound.

Summary form only given. We present an edge-preserving image compression technique based on the wavelet transform and iterative constrained least square regularization. This approach treats image reconstruction from lossy image compression as the process of image restoration. It utilizes the edge information detected from the source image as a priori knowledge for the subsequent reconstruction. Image restoration refers to the problem of estimating the source image from its degraded version. The reconstruction of DWT-coded images is formulated as a regularized image recovery problem and makes use of the edge information as the a priori knowledge about the source image to recover the details, as well as to reduce the ringing artifact of the DWT-coded image. To compromise the rate of edge information and DWT-coded image data, a scheme based on generalized finite automata (GFA) is used. GFA is used instead of vector quantization in order to achieve adaptive encoding of the edge image.

Summary form only given. We propose a hybrid image compression scheme based on wavelet transform, HVS thresholding and L/sub /spl infin//-constrained adaptive context modelling. This hybrid system combines the strengths of the wavelet transform, the HVS thresholding and the adaptive context modelling to result in a near optimal compression scheme. The wavelet transform is very powerful in localizing the global spatial and frequency correlation. The HVS model-based thresholding is designed to exploit and eliminate the wavelet coefficients insensitive to the human visual system. The context-based modelling is superior in decorrelating the local redundancy. In the scheme, the image is first decomposed into the multiresolution subimages using the orthogonal wavelet transform; each subimage corresponds to a octave band in the wavelet decomposition. The coefficients in the high-pass octave bands of the wavelet transform are then quantized through HVS frequency- and spatial model-based thresholding and vector quantization into wavelet decomposition with only significant coefficients to the HVS retained. In this HVS quantized wavelet decomposition, the coefficients insignificant to the human visual system are normalized to zero and the global spatial and frequency correlation are exploited and removed. Then the quantized subimages in the low-pass band and the remaining high-pass octave bands of each octave level are processed using the L/sub /spl infin//-constrained CALIC to de-correlate the local redundancy. It is demonstrated that the hybrid scheme is one of the best compression schemes in achieving the excellent compression rates and competitive PSNR while maintaining a small visual distortion. In comparing with the original CALIC, we were able to increase the PSNR by 0.65 dB or more and obtain bit rates 15 percent lower than the latter. We were also able to obtain competitive PSNR results against the best wavelet coders, while maintaining a smaller visual distortion. In particular, the wavelet CALIC was able to obtain 1.34 to 7.84 dB higher PSNR on the standard ISO test benchmarks than the SPIHT, one of the best wavelet coder.

Hidden Markov model (HMM) is a robust statistical methodology for automatic speech recognition. It has been tested in a wide range of applications. A prediction approach traditionally applied for the text compression and coding. Prediction by partial matching (PPM) which is a finite-context statistical modeling technique and can predict the next characters based on the context has shown a great potential in developing novel solutions to several language modeling problems in speech recognition. These two different approaches have their own spatial features respectively contributing to speech recognition. However, no work has been reported in integrating them at an attempt to form a hybrid speech recognition scheme. Taking the advantages of these two approaches, we propose a hybrid speech recognition model based on HMM and fuzzy PPM. The competitive and promising performance of the approach in speech recognition has been demonstrated by an experiment.

Portfolio theory considers a sequential portfolio selection procedure with the goal of best analyzing and predicting the performance of the market. Data compression techniques such as context-based modeling and vector quantization are based on a memoryless or near-memoryless prediction process and suit for the memoryless nature of the stock market data. In this paper, we propose a data compression-based portfolio prediction model hybridized with the fuzzy logic and genetic algorithm. This model exploits the prediction power of the lossy compression techniques such as vector quantization and context-based modeling and applies the prediction to the portfolio theory. In the model, the quantifiable microeconomic stock data are first optimized through the genetic algorithms to generate the most effective microeconomic data in relation to the stock market performance. The nonquantifiable microeconomic data are modeled with the fuzzification process. Then the previous stock market performance with the effective stock data and the fuzzified microeconomic data are processed based on the context-based modeling and vector quantization. Finally, the prediction of the stock market performance with the stock data is defuzzified using the fuzzification model to produce a portfolio performance prediction. The experiment on JF Asean Unit Trust using this portfolio theory model has shown a reliable prediction for the performance of the Trust for the past 5 years.

Image mosaics is one of the scene encoding approaches and is a very active research area in computer vision community and computer graphics community. This paper presents a robust panoramic image mosaicing scheme which employs complex wavelet pyramid and the human visual perceptive thresholding techniques. Complex wavelet transform guarantees not only a global optimal solution, but also scale and translation invariance for image alignment. Applying HVS (Human Visual System) thresholding for wavelet coefficients shows that image can be compressed significantly (15-30.1) while the reconstructed images are visually indistinguishable. The stitched images can be progressively refined on the multiresolution decomposition. This guarantees that the scheme has higher performance than traditional mosaicing techniques. Additionally, the scheme registers images taken directly by hand-held digital camera without knowing camera motion and any intrinsic parameters of camera. The present paper addresses the problems of both image matching and registration automatically and simultaneously.

We study high-fidelity image compression with a given tight bound on the maximum error magnitude. We propose some practical adaptive context modeling techniques to correct prediction biases caused by quantizing prediction residues, a problem common to the current DPCM like predictive nearly-lossless image coders. By incorporating the proposed techniques into the nearly-lossless version of CALIC, we were able to increase its PSNR by 1 dB or more and/or reduce its bit rate by ten per cent or more. More encouragingly, at bit rates around 1.25 bpp our method obtained competitive PSNR results against the best wavelet coders, while obtaining much smaller maximum error magnitude.