In tooth contour extraction there is insufficient intensity difference in x-ray images between the tooth and dental bone. This difference must be enhanced in order to improve the accuracy of tooth segmentation. This paper proposes a method to improve the intensity between the tooth and dental bone. This method consists of an estimation of tooth orientation (intensity projection, smoothing filter, and peak detection) and PCA-Stacked Gabor with ellipse Gabor banks. Tooth orientation estimation is performed to determine the angle of a single oriented tooth. PCA-Stacked Gabor with ellipse Gabor banks is then used, in particular to enhance the border between the tooth and dental bone. Finally, active contour extraction is performed in order to determine tooth contour. In the experiment, in comparison with the conventional active contour without edge (ACWE) method, the average mean square error (MSE) values of extracted tooth contour points are reduced from 26.93% and 16.02% to 19.07% and 13.42% for tooth x-ray type I and type H images, respectively.

This paper presents a new technique to smooth speech feature vectors for text-independent speaker verification using an adaptive band-pass IIR filer. The filter is designed by considering the probability density of modulation-frequency components of an M-dimensional feature vector. Each dimension of the feature vector is processed and filtered separately. Initial filter parameters, low-cut-off and high-cut-off frequencies, are first determined by the global mean of the probability densities computed from all feature vectors of a given speech utterance. Then, the cut-off frequencies are adapted over time, i.e. every frame vector, in both low-frequency and high-frequency bands based also on the global mean and the standard deviation of feature vectors. The filtered feature vectors are used in a SVM-GMM Supervector speaker verification system. The NIST Speaker Recognition Evaluation 2006 (SRE06) core-test is used in evaluation. Experimental results show that the proposed technique clearly outperforms a baseline system using a conventional RelAtive SpecTrA (RASTA) filter.

There are many web sites where net users can post and distribute their illustration images. A typical way to draw a digital illustration is first to draw rough lines on a paper and then to trace the lines on a graphics-tablet by hand. The input lines usually contain fluctuation due to hand-drawing, which limits the quality of illustration. Therefore, it is important to remove the fluctuation and to smooth the lines while maintaining sharp features such as corners. Although naive applications of moving average filters can smooth input lines, they may cause over-smoothing artifacts in which sharp features are lost by the filtering. This paper describes an improved line smoothing method using adaptive moving averages, which smoothes input lines while keeping high curvature points. The proposed method evaluates curvatures of input lines and adaptively controls the filter-size to reduce the over-smoothing artifacts. Experiments demonstrated advantages of the proposed method over the previous method in terms of achieving smoothing effect while still preserving sharp feature preservation.

Poor illumination and viewing conditions have negativeinfluences on the quality of an image, especially the contrast of the dark and bright region. Thus, captured and displayed images usually need contrast enhancement. Histogram-based or gamma correction-based methods are generally utilized for this. However, these methods are global contrast enhancement method, and since the sensitivity of the human eye changes locally according to the position of the object and the illumination in the scene, the global contrast enhancement methods have a limit. The spatial adaptive method is needed to overcome these limitations and it has led to the development of an integrated surround retinex (ISR), and estimation of dominant chromaticity (EDC) methods. However, these methods are based on Gray-World Assumption, and they use a general image formation model, so the color constancy is known to get poor results, shown through graying-out, halo-artifacts (ringing effects), and the dominated color. This paper presents a contrast enhancement method using a modified image formation model in which the image is divided into three components: global illumination, local illumination and reflectance. After applying the power constant value to control the contrast in the resulting image, the output image is obtained from their product to avoid or minimize a color distortion, based on the sRGB color representation. The experimental results show that the proposed method yields better performances than conventional methods.

Many researchers have proposed ultrasound imaging techniques for product inspection; however, most of these techniques are aimed at detecting the existence of flaws in products. The acquisition of an accurate three-dimensional image using ultrasound has the potential to be a useful product inspection tool. In this paper we apply the Envelope algorithm, which was originally proposed for accurate UWB (Ultra Wide-Band) radar imaging systems, to ultrasound imaging. We show that the Envelope algorithm results in image deterioration, because it is difficult for ultrasound measurements to achieve high signal to noise (S/N) ratio values as a result of a high level of noise and interference from the environment. To reduce errors, we propose two adaptive smoothing techniques that effectively stabilize the estimated image produced by the Envelope algorithm. An experimental study verifies that the proposed imaging algorithm has accurate 3-D imaging capability with a mean error of 6.1 µm, where the transmit center frequency is 2.0 MHz and the S/N ratio is 23 dB. These results demonstrate the robustness of the proposed imaging algorithm compared with a conventional Envelope algorithm.

A method based on covariance differencing for a uniform linear array is proposed to counter the problem of direction finding of narrowband signals under a colored noise environment. By assuming a Hermitian symmetric Toeplitz matrix for the unknown noise, the array covariance matrix is transformed into a centrohermitian matrix in an appropriate way allowing the noise component to be eliminated. The modified covariance differencing algorithm provides accurate direction of arrival (DOA) estimation when the incident signals are uncorrelated or just two of the signals are coherent. If there are more than two coherent signals, the presented method combined with spatial smoothing (SS) scheme can be used. Unlike the original method, the new approach dispenses the need to determine the true angles and the phantom angles. Simulation results demonstrate the effectiveness of presented algorithm.

In this paper, we propose a new motion vector smoothing algorithm using weighted vector median filtering based on edge direction for frame interpolation. The proposed WVM (Weighted Vector Median) system adjusts the weighting values based on edge direction, which is derived from spatial coherence between the edge direction continuity of a moving object and motion vector (MV) reliability. The edge based weighting scheme removes the effect of outliers and irregular MVs from the MV smoothing process. Simulation results show that the proposed algorithm can correct wrong motion vectors and thus improve both the subjective and objective visual quality compared with conventional methods.

We proposed a new motion vector (MV) smoothing using fuzzy weighting and vector median filtering for frame rate up-conversion. A fuzzy reasoning system adjusts the weighting values based on the local characteristics of MV field including block difference and block boundary distortion. The fuzzy weighting removes the affect of outliers and irregular MVs from the MV smoothing process. The simulation results show that the proposed algorithm can efficiently correct wrong MVs and thus improve visual quality of the interpolated frames better than conventional methods.

Delivering video streaming service over the Internet encounters some challenges. Two of them are heterogeneity of networks capacity and variability of video data rate. The capacity of network segments are constrained. Meanwhile, the rate of video data to be transmitted is highly variable in order to get near-constant images quality. Therefore, to send variable bit rate (VBR) video data over capacity-constrained network, its bit rate should be adjusted. In this paper a system to adjust the transmission bit rate of VBR MPEG video data called Transcoding-after-Smoothing (TaS), which is a combination of bit rate transcoding and bit rate smoothing algorithm, is proposed. The system smoothes out transmission rate of video data while at the same time also performs transcoding on some video frames when necessary in order to keep the transmission bit rate below a certain threshold value. Two kinds of TaS methods are proposed. One method does not have transcoding preference, while the other method uses frame type preference where an intra-coded frame is the last one that will be transcoded. These methods are implemented in our video server where a VBR video data is accessed by a client. Our experiment results show that the first TaS method yields significant reduction in the number of transcoded frames compared with the second TaS method and conventional frame-level transcoding. However, the second method performs better in minimizing the quality distortion.

Programming the multiprocessor system-on-chip (MPSoC) requires partitioning the sequential reference programs onto multiple processors running in parallel. However, designers still need to partition the code manually due to the lack of automated partition techniques. To settle this issue, this paper proposes a partition exploration algorithm based on the search space smoothing techniques, and implements the proposed method using a commercial extensible processor (Xtensa LX2 processor from Tensilica Inc.). We have verified the feasibility of the algorithm by implementing the MPEG2 benchmark on the Xtensa-based two-processor system. The final experimental results indicate a performance improvement of at least 1.6 compared to the single-processor system.

In this letter, a new estimation filtering is proposed when a delay between signal generation and signal estimation exists. The estimation filter is developed under a maximum likelihood criterion using only the finite observations on the delay interval. The proposed estimation filter is represented in both matrix form and iterative form. It is shown that the filtered estimate has good inherent properties such as time-invariance, unbiasedness and deadbeat. Via numerical simulations, the performance of the proposed estimation filtering is evaluated by the comparison with that of the existing fixed-lag smoothing, which shows that the proposed approach could be appropriate for fast estimation of signals that vary relatively quickly. Moreover, the on-line computational complexity of the proposed estimation filter is shown to be maintained at a lower level than the existing one.

A novel RSSI (Received Signal Strength Indication) refinement algorithm is proposed to enhance the resolution for indoor and outdoor real-time location tracking system. The proposed refinement algorithm is implemented in two separate phases. During the first phase, called the pre-processing step, RSSI values at different static locations are collected and processed to build a calibrated model for each reference node. Different measurement campaigns pertinent to each parameter in the model are implemented to analyze the sensitivity of RSSI. The propagation models constructed for each reference nodes are needed by the second phase. During the next phase, called the runtime process, real-time tracking is performed. Smoothing algorithm is proposed to minimize the dynamic fluctuation of radio signal received from each reference node when the mobile target is moving. Filtered RSSI values are converted to distances using formula calibrated in the first phase. Finally, an iterative trilateration algorithm is used for position estimation. Experiments relevant to the optimization algorithm are carried out in both indoor and outdoor environments and the results validated the feasibility of proposed algorithm in reducing the dynamic fluctuation for more accurate position estimation.

Orthogonal frequency-division multiplexing (OFDM) systems often use a cyclic prefix (CP) to simplify the equalization design at the cost of bandwidth efficiency. To increase the bandwidth efficiency, we study the blind equalization with linear smoothing [1] for single-input multiple-output (SIMO) OFDM systems without CP insertion in this paper. Due to the block Toeplitz structure of channel matrix, the block matrix scheme is applied to the linear smoothing channel estimation, which equivalently increases the number of sample vectors and thus reduces the perturbation of sample autocorrelation matrix. Compared with the linear smoothing and subspace methods, the proposed block linear smoothing requires the lowest computational complexity. Computer simulations show that the block linear smoothing yields a channel estimation error smaller than that from linear smoothing, and close to that of the subspace method. Evaluating by the minimum mean-square error (MMSE) equalizer, the block linear smoothing and subspace methods have nearly the same bit-error-rates (BERs).

Although subspace-based methods for estimating the Angle of Arrival (AOA) require a precise array response to achieve highly accurate results, it is difficult to obtain this response in practice even though the antennas are calibrated. Therefore, a method of compensating for errors in calibration is required. This paper proposes a procedure to enable precise AOA estimates to be obtained in a real system by applying array calibration and spatial smoothing preprocessing (SSP). Measured data were collected from experiments using two scenarios, i.e., in an anechoic chamber and at an open site, where a single source signal arrived at the array antenna. All measured data were then calibrated by using data obtained at 0 deg in an anechoic chamber before the AOAs were estimated. Nevertheless, errors in the array response remained after calibration because errors in the AOA estimates could still be observed. SSP was then applied to the calibrated data to obtain more accurate AOA estimates. We found that SSP can reduce the random error in an array response obtained in a real system, leading to reduced errors in AOA estimates in the observed data. To generalize the problem that SSP can reduce random perturbation in the array response, simple expressions are illustrated and verified by Monte-Carlo simulation. Random gain and phase errors in the array response are only considered in this paper and ESPRIT was used to estimate the AOAs.

Direction of arrival estimation of coherent multipath waves by using superresolution technique often requires decorrelation preprocessings. Spatial smoothing preprocessings are the most popular schemes as the techniques. In mobile environment, position change of the target/transmitter often brings us decorrelation effect. In addition, multiple signals transmitted by an antenna array, such as a MIMO transmitter, can also cause the same effect. These effects can be categorized as the spatial smoothing preprocessing at the transmitter. In this paper, we analyze the spatial smoothing effect at the transmitter in the presence of multipath coherent waves. Theoretical and simulation results show that the spatial smoothing at the transmitter has a good feature in comparison with the conventional SSP at the receiving array. We also show that better decorrelation performance can be obtained when the SSPs at the transmitter and receiving array are applied simultaneously.

Burst assembly at edge nodes is an important issue for the Optical Burst Switching (OBS) networks because it has a great impact on the traffic characteristics. We analyze the assembled traffic of the Science Information Network (SINET) by using the Fractional Brownian Motion (FBM) model. The analytical and simulation results show that existing assembly schemes cannot avoid increasing the burstiness, which will deteriorate the network performance. Here, burstiness is defined as the variance of the bitrate in small timescales. Therefore, we address the issue of how to reduce the burstiness of the assembled network traffic. Firstly, a sliding window-based assembly algorithm is introduced to reduce the burstiness of assembled traffic by transmitting bursts at an average rate in a small timescale. Next, an advanced timer-based assembly algorithm is introduced, by which the traffic rate is smoothed out by restricting the burst length to a threshold. The simulation results show that both the sliding window-based and advanced timer-based assembly algorithms perform better than existing assembly algorithms do in terms of the burst loss ratio. The simulation also indicates that the advanced timer-based assembly algorithm performs better in terms of the edge buffering delay than the sliding window-based assembly algorithm does.

This paper describes a novel parameter generation algorithm for an HMM-based speech synthesis technique. The conventional algorithm generates a parameter trajectory of static features that maximizes the likelihood of a given HMM for the parameter sequence consisting of the static and dynamic features under an explicit constraint between those two features. The generated trajectory is often excessively smoothed due to the statistical processing. Using the over-smoothed speech parameters usually causes muffled sounds. In order to alleviate the over-smoothing effect, we propose a generation algorithm considering not only the HMM likelihood maximized in the conventional algorithm but also a likelihood for a global variance (GV) of the generated trajectory. The latter likelihood works as a penalty for the over-smoothing, i.e., a reduction of the GV of the generated trajectory. The result of a perceptual evaluation demonstrates that the proposed algorithm causes considerably large improvements in the naturalness of synthetic speech.

This paper discusses the least-squares linear filtering and smoothing (fixed-point and fixed-interval) problems of discrete-time signals from observations, perturbed by additive white noise, which can be randomly delayed by one sampling time. It is assumed that the Bernoulli random variables characterizing delay measurements are correlated in consecutive time instants. The marginal distribution of each of these variables, specified by the probability of a delay in the measurement, as well as their correlation function, are known. Using an innovation approach, the filtering, fixed-point and fixed-interval smoothing recursive algorithms are obtained without requiring the state-space model generating the signal; they use only the covariance functions of the signal and the noise, the delay probabilities and the correlation function of the Bernoulli variables. The algorithms are applied to a particular transmission model with stand-by sensors for the immediate replacement of a failed unit.

We proposed and evaluated a speech packet loss concealment method which predicts lost segments from speech included in packets either before, or both before and after the lost packet. The lost segments are predicted recursively by using linear prediction both in the forward direction from the packet preceding the loss, and in the backward direction from the packet succeeding the lost segment. Predicted samples in each direction are smoothed by averaging using linear weights to obtain the final interpolated signal. The adjacent segments are also smoothed extensively to significantly reduce the speech quality discontinuity between the interpolated signal and the received speech signal. Subjective quality comparisons between the proposed method and the the packet loss concealment algorithm described in the ITU standard G.711 Appendix I showed similar scores up to about 10% packet loss. However, the proposed method showed higher scores above this loss rate, with Mean Opinion Score rating exceeding 2.4, even at an extremely high packet loss rate of 30%. Packet loss concealment of speech degraded with G.729 coding, and babble noise mixed speech showed similar trends, with the proposed method showing higher qualities at high loss rates. We plan to further improve the performance by using adaptive LPC prediction order depending on the estimated pitch, and adaptive LPC bandwidth expansion depending on the consecutive number of repetitive prediction, among many other improvements. We also plan to investigate complexity reduction using gradient LPC coefficient updates, and processing delay reduction using adaptive forward/bidirectional prediction modes depending on the measured packet loss ratio.

A rate splitting algorithm is presented for a multiple video transmission system to transfer the aggregation (or statical multiplexing) of multiple video streams to multiple clients so that each client can receive the requested video stream with the reliable fidelity. Computer simulations for transmission of a set of 128 MPEG compressed video streams show that the proposed algorithm alleviates the variability of the aggregate video transmission comparing with a scheme to smooth individually each of videos using the traditional online smoothing algorithm. Besides, the proposed is 2 time faster than the traditional one.