Visual-speech-pass filtering for robust automatic lip-reading

This paper proposes a temporal filtering technique used in extraction of visual features for improved robustness of automatic lip-reading, called visual-speech-pass filtering. A band-pass filter is applied to the pixel value sequence of the images containing the speaker’s lip region to remove unwanted variations that are not relevant to the speech information. The filter is carefully designed based on psychological, spectral, and experimental analyses. Experimental results on two speaker-independent and one speaker-dependent recognition tasks demonstrate that the proposed technique significantly improves recognition performance in both clean and visually noisy conditions.     

Efficient robust filtering technique for blocking artifacts reduction

This paper presents a new post-processing algorithm based on a robust statistical model to remove the blocking artifacts observed in block discrete cosine transform (BDCT)-based image compression standards. The novelty is the implementation of a new robust weight function for the block artifact reduction. The blocking artifacts in an image are treated as an outlier random variable. The robust formulation aims at eliminating the artifacts outliers, while preserving the edge structures in the restored image. Extensive simulation results and comparative studies demonstrate that the presented method provides superior results in terms of pixel-wise (PSNR) and perceptual (SSIM) measures.     

Unsupervised strategies for shilling detection and robust collaborative filtering

Collaborative filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in previous work. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide an in-depth analysis of shilling profiles and describe new approaches to detect malicious collaborative filtering profiles. In particular, we exploit the similarity structure in shilling user profiles to separate them from normal user profiles using unsupervised dimensionality reduction. We present two detection algorithms; one based on PCA, while the other uses PLSA. Experimental results show a much improved detection precision over existing methods without the usage of additional training time required for supervised approaches. Finally, we present a novel and highly effective robust collaborative filtering algorithm which uses ideas presented in the detection algorithms using principal component analysis.     

Biologically motivated space-variant filtering for robust optic flow processing

We describe and test a biologically motivated space-variant filtering method for decreasing the noise in optic flow fields. Our filter model adopts certain properties of a particular motion-sensitive area of the brain (area MT), which averages the incoming motion signals over receptive fields, the sizes of which increase with the distance from the center of the projection. We use heading estimation from optic flow as a criterion to evaluate the improvement of the filtered flow field. The tests are conducted on flow fields calculated with a standard flow algorithm from image sequences. We use two different sets of image sequences. The first set is recorded by a camera which is installed in a moving car. The second set is derived from a database containing three dimensional data and reflectance information from natural scenes. The latter set guarantees full control of the camera motion and ground truth about the flow field and the heading. We test the space-variant filtering method by comparing heading estimation results between space-variant filtered flow, flow filtered by averaging over domains of the visual field with constant size (constant filtering) and raw unfiltered flow. Because of noise and the aperture problem the heading estimates obtained from the raw flows are often unreliable. Estimated heading differs widely for different sub-sampled calculations. In contrast, the results obtained from the filtered flows are much less variable and therefore more consistent. Furthermore, we find a significant improvement of the results obtained from the space-variant filtered flow compared to the constant filtered flow. We suggest extensions to the space-variant filtering procedure that take other properties of motion representation in area MT into account.     

Design of a robust estimator for nonlinear kinetic modelling

A biased estimator for nonlinear kinetic modelling is studied. The proposed estimator contains two easily tuned parameters that enable the estimator to possess robustness in dealing with the problem of a nearly singular design matrix. The robustness of the estimator is discussed in detail, and the asymptotic normality of the estimator is proved concisely but rigorously. The regularity conditions imposed to obtain the results are fairly weak in engineering applications. Three examples presented demonstrate that the estimator is significantly better than the least-squares (LS) estimator and the ridge estimator in both estimate accuracy and convergence speed     

Fuzzy control-based real-time robust balance for a humanoid robot

This paper studies and implements a real-time robust balance control for a humanoid robot under three environment disturbances which are an external thrust, an inclinable platform, and a see-saw. More precisely to say, the robot with robust control can resist an external thrust, stand on a two-axis inclinable platform, or walk on a see-saw successfully. The main feature of the robot is that it has a waist joint which has three degrees of freedom. With the aids of the proposed fuzzy controllers, the robot can change the posture of the body nimbly by adjusting the waist joint and two ankle joints to strengthen the stabilization capacity. The sensory system of the robot includes eight force sensors and one inertial measurement unit sensor in order to measure the center of pressure and the slant angle of the robot’s body. According to the measured data from the sensors and by imitating human reflex actions, the proposed fuzzy controllers perform real-time balance control for the robot under three environment disturbances. According to the experiment results, the stability of the robot is increased at least 32.2 and 61.7% under the first two environment disturbances, respectively. In addition, the robot walking on a see-saw has a success rate of about 95%.     

Fuzzy modelling and simulation for aggregate production planning

The market demands and financial constraints in medium- to long-term production planning are often subject to uncertainties. As an extension of the author's previous paper, this paper focuses on a formulation and simulation analysis for multi-product aggregate production-planning problems with fuzzy demands and fuzzy capacities. The fuzzy production–inventory balance equation is formulated as a soft equation and can be interpreted as the possibility level of meeting the market demands, which is more meaningful and accepted by the practitioner than the previous one. With this interpretation, a fuzzy multi-product aggregate production-planning model is transformed into a parametric programming model. A simulation of a practical instance is conducted to illustrate the model and demonstrate the performance and effect of various parameters on the optimal aggregate production plan. The proposed formulation and simulation analysis can help the decision-maker make a reasonable and preferred aggregate plan to guarantee feasibility of the downstream family disaggregation plans.     

Fuzzy successive modelling and control for time-delay systems

In this paper, a procedure for constructing a fuzzy model of a given system associated with a digital fuzzy filter is proposed. Based on the structure of the Smith predictor, a hybrid model predictive control approach is also presented. Two experimental results using (1) an analogue simulator and (2) a blower temperature control rig have been illustrated. It shows that the proposed control structure is a feasible approach to control systems with time-delay.     

A robust neuro-fuzzy network approach to impulse noise filtering for color images

Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, an effective impulse noise filter for color images is presented. It consists of two modes of operation, namely, training and testing (filtering). During training, the impulse detector is used to locate the noisy pixels in the color images for optimizing the RNF network. During testing, if a pixel is detected as a corrupted one according to the impulse detector, the trained RNF network will be triggered to output a new pixel to replace it. The proposed impulse noise filter is distinguished by two properties. The first is the use of a simple impulse detector, which is efficient and yet effective in detecting the noisy pixels in color images. The other is the use of a novel membership function in the design of the adaptive RNF network, making the network robust to impulse noise. As demonstrated by the experimental results, the proposed filter not only has the abilities of noise attenuation and details preservation but also possesses desirable robustness and adaptive capabilities. It outperforms other conventional multichannel filters.     

Geometric particle swarm optimization for robust visual ego-motion estimation via particle filtering

Conventional particle filtering-based visual ego-motion estimation or visual odometry often suffers from large local linearization errors in the case of abrupt camera motion. The main contribution of this paper is to present a novel particle filtering-based visual ego-motion estimation algorithm that is especially robust to the abrupt camera motion. The robustness to the abrupt camera motion is achieved by multi-layered importance sampling via particle swarm optimization (PSO), which iteratively moves particles to higher likelihood region without local linearization of the measurement equation. Furthermore, we make the proposed visual ego-motion estimation algorithm in real-time by reformulating the conventional vector space PSO algorithm in consideration of the geometry of the special Euclidean group SE(3), which is a Lie group representing the space of 3-D camera poses. The performance of our proposed algorithm is experimentally evaluated and compared with the local linearization and unscented particle filter-based visual ego-motion estimation algorithms on both simulated and real data sets.     

A robust filtering algorithm for subpixel reconstruction of chain coded line drawings

A robust algorithm is presented for smoothing and achieving subpixel accuracy in the reconstruction of chain coded line drawings. The algorithm does not remove sharp corners and does not need a priori knowledge of curvature statistics. A fast on-line implementation can be achieved using a table look-up. A simplified algorithm can be used for reconstructing digitized polygons.     

Data-driven process decomposition and robust online distributed modelling for large-scale processes

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.     

多新息抗差-自适应卡尔曼滤波定位算法研究

针对移动机器人在定位过程中,由传感器测量误差和机器人模型引起的位姿误差导致系统定位精度急剧下降的问题,提出了一种多新息卡尔曼滤波算法.在标准卡尔曼滤波的基础上,当传感器测量值存在误差时,引入抗差权因子,通过改变误差测量值的权值提高滤波器的估计精度;当机器人位姿存在误差时,引入自适应因子,通过调整状态协方差矩阵的大小抵制位姿误差引起的滤波发散.同时,引入了多新息,即多个时刻的新息向量,进一步提高此非线性系统的精度.实验表明:当存在测量误差和位姿误差时,该滤波算法能有效提高定位精度.     

离散系统方差约束鲁棒H2/H∞滤波的新算法

考虑带有稳态误差方差约束的线性受扰系统的鲁棒H2/H∞滤波问题.引入了广义逆矩阵,提出了一个新的算法.通过直接解两个Riccati方程后,获得滤波器,并且同时满足3个性能要求:滤波过程是渐近稳定的;每个状态的稳态估计误差方差不超过规定的上界;从外部噪声输入到误差状态输出的传递函数的H∞范数满足规定的上界.一个数字例子说明了这种设计方法的有效性.     

New results in modelling derived from Bayesian filtering

This paper suggests an original heuristic modelling algorithm expressed in terms of homogenous combinations of the classical system dynamics and the Bayesian degree of truth employed in modelling. The main benefits of the proposed approach compared to classical modelling are the increased transparency and alleviated computational time. Two case studies, dealing with a mobile robot and an unforced pendulum system, are included to exemplify and test the theoretical results. One of the case studies makes use of the definition and calculation of several discrete plausibilities.     

Fuzzy spectral clustering with robust spatial information for image segmentation

In recent years, spectral clustering has become one of the most popular clustering algorithms in areas of pattern analysis and recognition. This algorithm uses the eigenvalues and eigenvectors of a normalized similarity matrix to partition the data, and is simple to implement. However, when the image is corrupted by noise, spectral clustering cannot obtain satisfying segmentation performance. In order to overcome the noise sensitivity of the standard spectral clustering algorithm, a novel fuzzy spectral clustering algorithm with robust spatial information for image segmentation (FSC_RS) is proposed in this paper. Firstly, a non-local-weighted sum image of the original image is generated by utilizing the pixels with a similar configuration of each pixel. Then a robust gray-based fuzzy similarity measure is defined by using the fuzzy membership values among gray values in the new generated image. Thus, the similarity matrix obtained by this measure is only dependent on the number of the gray-levels and can be easily stored. Finally, the spectral graph partitioning method can be applied to this similarity matrix to group the gray values of the new generated image and then the corresponding pixels in the image are reclassified to obtain the final segmentation result. Some segmentation experiments on synthetic and real images show that the proposed method outperforms traditional spectral clustering methods and spatial fuzzy clustering in efficiency and robustness.     

Fuzzy modelling for controlled anaesthesia in hospital operating theatres

Balanced anaesthesia introduces the problem of drug interactions. In this paper, the effect concentrations of anaesthetic and analgesic drugs are used to model the pharmacodynamic interactions of the two drugs on the cardiovascular parameters, and on the auditory evoked potentials. An adaptive network-based fuzzy inference system is used to model the different signals. A stimulus model is used to establish the effects of surgical stimulus on the cardiovascular parameters. This model is constructed into a Mamdani type of fuzzy model, using anaesthetist's knowledge described by fuzzy IF–THEN rules. Clinical data are used to construct the patient model.     

On the robust detection of edges in time series filtering

Abrupt shifts in the level of a time series represent important information and should be preserved in statistical signal extraction. Various rules for detecting level shifts that are resistant to outliers and which work with only a short time delay are investigated. The properties of robustified versions of the t-test for two independent samples and its non-parametric alternatives are elaborated under different types of noise. Trimmed t-tests, median comparisons, robustified rank and ANOVA tests based on robust scale estimators are compared.