A New Adaptive Threshold Algorithm to Speech Enhancement Based on Minimum Description Length Criterion

We present a novel approach for clip-based key frame extraction. Our framework allows both clips with subtle changes as well as clips containing rapid shot changes, fades and dissolves to be well approximated. We show that creating key frame video abstractions can be achieved by transforming each frame of a video sequence into an eigenspace and then clustering this space using Gaussian mixture models (GMMs). A minimum description length (MDL) criterion is then used to determine the optimal number of GMM components to use in the clustering. The image nearest to the centres of each of the GMM components are selected as key frames. Unlike previous work, this technique relies on global video clip properties and results show that the key frames extracted give a very good representation of the overall clip content. We demonstrate the application of this technique on a database of 307 clips of wildlife footage containing dissolves, shot changes, fades, pans, zooms and a wide range of animal behaviours.

This paper presents a new technique for region-oriented video compression based on the minimization of a minimum description length (MDL) criterion. The proposed scheme is based on a region-based motion compensation approach, in which the displaced frame difference (DFD) is coded together with the segmentation map and the motion parameters. The region-based segmentation is approximated by a polygonal representation which permits us to significantly reduce the segmentation coding cost. The polygonal approximation, which minimizes the MDL criterion, is considered the optimal approximation in terms of compression performance. In this contest, the MDL criterion has been developed and tested for the three following DFD coding techniques: (1) entropy coding of the quantized DFD image; (2) entropy coding of the quantized DFD values obtained after morphological filtering of the DFD image; and (3) DCT-based coding of the DFD image. The experimental results, presented on real video sequences, validate the proposed approach.

In this paper a new non-predictive wavelet based video coding scheme is presented. The scheme omits motion estimation/compensation process (prediction step) in the temporal axis. The video sequence is grouped into a number of frames. An adaptive thresholding algorithm is used to optimize the subband threshold settings, and use to identify the significance of the subband coefficients. The significant vectors of the same subband within the group frames are joined together in order to exploit their spatial-temporal redundancies. The significant vectors are quantized using a novel multistage lattice vector quantization (MLVQ). This process reduces quantization errors and enhances the reconstructed frame quality. Incremental results of the new non-predictive video coding scheme with adaptive thresholding algorithm are obtained with more than 1 dB. Also it is significantly better and than MPEG-2 with I frames only for several test sequences.

This paper presents a new technique for region-oriented video compression based on the minimization of a minimum description length (MDL) criterion. The proposed video compression scheme is based on a region-based motion compensation approach in which the displaced frame difference is partially coded together with the segmentation and the motion parameters. The region-based segmentation is approximated by a polygonal representation which permits to significantly reduce the segmentation coding cost. The polygonal approximation which minimizes the MDL criterion is finally considered as optimal. Experimental results are presented on real video sequences.

Blind source separation algorithm is usually not able to estimate the number of unknown signal sources. In many occasions, the number of source signal is unknown and may even be in dynamic changes. This paper has achieved to estimate the number of sources and real-time tracking using subspace method in the over-determined blind source separation, while the number of sources is unknown and dynamic . The first section is the estimation of the rank of signal subspace and the second section is about the subspace tracking algorithm. The subspace method is to separate the observed sensor signals into signal subspace and noise subspace. This will not only greatly reduce the noise, but also can estimate the number of active source signals by the measurement of eigenvalues. To achieve the real-time adjustment of the threshold in the dynamic blind source processing with Akaike’s information criterion (AIC) and the minimum description length criterion (MDL).

In this paper, an adaptive subspace classification technique using a fuzzy support vector machine (FSVM) is proposed for subspace-based TOA estimation in a multipath channel. For the subspace-based algorithm to work properly, the signal subspace should be correctly estimated from a data matrix composed of the received samples. Through SVD on the data matrix, singular values and vectors are derived in pairs. According to the derived singular values, the corresponding singular vectors are selected as a set of basis-spanning signal subspaces. Criteria such as the Akaike information criterion (AIC) and minimum description length criterion (MDLC) have previously been adopted for subspace classification. Instead of using these criteria, we propose an adaptive classification technique using a FSVM for improved subspace-based TOA estimation in a multipath channel.

The problem of modeling polynomial-phase signals is considered. Techniques based on the bootstrap and multiple hypotheses tests for optimal model selection of constant amplitude polynomial-phase signals embedded in stationary noise are developed. Phase parameter estimators based on both the least-squares method and the polynomial phase transform are used. The proposed techniques are compared with existing ones, including Akaike's (1978, 1974) information criterion and Rissanen's (1978) minimum description length criterion and are shown to outperform these procedures for the considered small sample sizes.

An important practical question, given various sources of noise in resting-state blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) images, is how much data to acquire to get identifiable and stable independent component analysis (ICA) networks in MRI scans. In this study, we investigate how ICA dimensionality, stability of the estimated independent components, and the spatiotemporal features of the extracted networks are affected by scan length. Our results show that the length of scan clearly influences the network dimensionality, peak voxel location of the spatial maps, spectral power, and functional network connectivity (FNC). Using minimum description length criteria and entropy-rate based order selection, we observed that the dimensionality decreased as the scan length decreased from 12 to 2 minutes. In addition, due to decreased dimensionality in shorter scans, some networks such as the ventral attention and subcortical networks could not be extracted. But the networks that could be identified in shorter scans including the default-mode, salience, and self-referential networks were stable (cluster quality index Iq > 0.8). Kurtosis of time-courses and spatial maps, as well as the spectral parameters (the dynamic range and fractional of amplitude of low frequency fluctuation; fALFF), and FNC also changed with the scan length with shorter scans having lower values. Consequently, scan length choice can have a significant impact on the of ICA-based restingstate fMRI studies. Future studies using larger sample sizes are warranted to further assess the implications of the scan length and data size on ICA outcome measures and reliability.

The combination of nonlocal grouping and transformed domain filtering has led to the state-of-the-art denoising techniques. In this paper, we extend this line of study to the denoising of synthetic aperture radar (SAR) images based on clustering the noisy image into disjoint local regions with similar spatial structure and denoising each region by the linear minimum mean-square error (LMMSE) filtering in principal component analysis (PCA) domain. Both clustering and denoising are performed on image patches. For clustering, to reduce dimensionality and resist the influence of noise, several leading principal components identified by the minimum description length criterion are used to feed the K-means clustering algorithm. For denoising, to avoid the limitations of the homomorphic approach, we build our denoising scheme on additive signal-dependent noise model and derive a PCA-based LMMSE denoising model for multiplicative noise. Denoised patches of all clusters are finally used to reconstruct the noise-free image. The experiments demonstrate that the proposed algorithm achieved better performance than the referenced state-of-the-art methods in terms of both noise reduction and image detail preservation.

The authors propose a two-dimensional direction-of-arrival (DOA) estimation for multi-path environments, in which there are uncorrelated, partially correlated and coherent signals. The inability to identify non-coherent signals from coherent ones results in a considerable waste of sensors. In this work, an adaptive and automated threshold is considered for the efficient separation of noise, non-coherent signals, and coherent groups. First, non-coherent signals, coherent groups, and noise subspace are separated using k-medoids clustering. After determining the number of sources, non-coherent signals and coherent groups, non-coherent DOAs are estimated separately. The number of coherent signals in each coherence group is determined by the minimum descriptive length criterion. Finally, coherent DOAs are estimated in each group by constructing a coherent estimation matrix. The proposed method does not require any prior information such as knowing the number of signals or the covariance matrix of uncorrelated signals. The simulation results show that the proposed method is able to distinguish between the non-coherent and coherent signals, even at low signal-to-noise ratios and a small number of snapshots. Also, in terms of detection probability and estimation accuracy, it shows an improvement of over 1.2 and 83%, respectively, compared with the conventional forward−backward spatial smoothing scheme.

This paper proposes a novel method to develop a process response model from continuous time-series data. The method consists of the following phases: (1) reciprocal correlation analysis; (2) process response model; (3) extraction of control rules; (4) extraction of a workflow; (5) detecting outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from learning classifier system using the minimum description length criteria and tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown the validity and the effectiveness

Abstract. In this study the cupola of San Gaudenzio’s Basilica in Novara, Italy, has been monitored by using two low-cost GNSS receivers located on the East and West side of the spire. Time series of daily solutions for an observation period of about one year have been collected and interpolated by cubic splines. The minimum description length criterion has been used to optimize this interpolation. The results show that the building had an uplift with a maximum amplitude of about 2 cm during summer. Moreover, from a joint analysis of the two points, one can realize that the uplift is not homogeneous, but the structure made some oscillations (with an amplitude at most of 4 mm) when rising up. As for the planimetric coordinates, the two antennas had a slightly different behaviour. The West point showed displacements at most of 1 cm and solutions with a very high repeatability of the order of few millimeters. The East point had a similar repeatability until a sudden jump occurred, followed by more noisy solutions in all the three directions. This noise degradation slowly dampened, till almost disappearing at the end of the recorded time series. This anomalous behaviour could be attributed to some structural movements. The test was successful in the sense that (1) it was proved that the millimeter accuracy can be reached by using GNSS low-cost receivers installed at San Gaudenzio’s cupola, even with a non-perfect sky visibility; (2) such an accuracy is able to show interesting movements of the cupola that can provide information about its stability.

This paper aims to describe the optimal autoregressive order of varying-length electromyograms for myopathic subjects. Epochs of electromyography signals are modeled as outputs of autoregressive systems, for orders varying from 1 to 100. The optimal order to represent each epoch is chosen by the minimum description length criterion. Probability density functions are fitted to the histograms of the optimal orders. The lognormal function provides the best fitting, and its mean value varies linearly with the epoch length.

A novel image segmentation algorithm based on the adaptive edge detection and an improved mean shift is proposed. According to the Ostu method, an adaptive threshold algorithm is applied to improve Canny operator in edge detection. The edge detection method has better performance and strong adaptability. Then the resulting edge information is incorporated into the main two steps of image segmentation based on mean shift. Since the discontinuity and homogeneity information are combined flexibly, the proposed algorithm takes the best of local and global image information. Experimental results reveal that the proposed algorithm is stronger adaptive and achieves better segmentation performance compared with several typical kinds of methods.

Proposes an efficient architecture for selective image modeling. The authors give an example in which models of different scale are reconstructed in parallel. It is shown that this redundant representation can effectively be pruned using the criterion of minimum description length. Models that are selected in the final description indicate the appropriate scale of observation. >