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This paper proposes a novel adaptive Super-Twisting sliding mode control for a microgyroscope under unknown model uncertainties and external disturbances. In order to improve the convergence rate of reaching the sliding surface and the accuracy of regulating and trajectory tracking, a high order Super-Twisting sliding mode control strategy is employed, which not only can combine the advantages of the traditional sliding mode control with the Super-Twisting sliding mode control, but also guarantee that the designed control system can reach the sliding surface and equilibrium point in a shorter finite time from any initial state and avoid chattering problems. In consideration of unknown parameters of micro gyroscope system, an adaptive algorithm based on Lyapunov stability theory is designed to estimate the unknown parameters and angular velocity of microgyroscope. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results. The comparative study between adaptive Super-Twisting sliding mode control and conventional sliding mode control demonstrate the superiority of the proposed method.

Jacquard image segmentation is one of the primary steps in image analysis for jacquard pattern identification. The main aim is to recognize homogeneous regions within a jacquard image as distinct, which belongs to different patterns. Active contour models have become popular for finding the contours of a pattern with a complex shape. However, the performance of active contour models is often inadequate under noisy environment. In this paper, a robust algorithm based on the Mumford-Shah model is proposed for the segmentation of noisy jacquard images. First, the Mumford-Shah model is discretized on piecewise linear finite element spaces to yield greater stability. Then, an iterative relaxation algorithm for numerically solving the discrete version of the model is presented. In this algorithm, an adaptive triangular mesh is refined to generate Delaunay type triangular mesh defined on structured triangulations, and then a quasi-Newton numerical method is applied to find the absolute minimum of the discrete model. Experimental results on noisy jacquard images demonstrated the efficacy of the proposed algorithm.

Mammography is currently regarded as one of the best ways to detect breast cancer in the early stage. However, due to the limitation in imaging condition and the subtleness of the difference between normal and abnormal features, it is generally difficult to interpret the mammograms. Thus, image enhancement techniques have been widely used in screening mammograms. In this paper, a multiscale contrast enhancement algorithm based on Laplacian Pyramid is developed to enhance the contrast of the mammograms and improve the discernibility of the abnormal features. In the proposed algorithm, an image is first decomposed into a multi-level Laplacian Pyramid and then the enhancement is performed in the reconstruction stage. A multiscale contrast measure is used to modify the coefficients iteratively level by level and the enhanced image is obtained at the lowest level. Experiments proved the effectiveness of the proposed algorithm.

Jacquard image denoising is an active research field in jacquard image processing and has attracted much attention in the past years. In this paper, we present a novel denoising algorithm within the framework of variational schemes and Beltrami manifold technique. Variational schemes for image denoising consist of minimizing a functional which incorporates both the data and a Beltrami flow term. The main advantage of the proposed algorithm is that it has both good noise reduction and edge protection capabilities. Experimental results show that the proposed method offers effective noise removal in real noisy jacquard images while maintaining fine structure of patterns.

The correct segmentation of textural pattern into different meaningful regions is one of the most important problems in automatic texture image recognition. In this paper, we presented a variational integration of shape prior statistics into a phase-field based segmentation process. By derivating the new phase field functionals with gradient shape policy, we obtain the interface evolution process for texture pattern. The interface evolving uses information from shape templates to guide its behavior to desired features. Experimental results on noisy texture images demonstrated the efficacy of the proposed algorithm.

Jacquard image inpainting is an interesting new research topic in pattern preprocessing for jacquard CAD. Phase field model has been well acknowledged as an important method for image inpainting. This paper discussed the problem of jacquard image inpainting by approaching the phase field paradigm from a numerical approximation perspective. The basic idea is to represent the damaged pattern of interest in implicit form, and fill-in the damaged parts with a system of geometric partial differential equations derived from phase-field model. The novelty of our approach lies primarily in exploiting explicitly the constraint enforced by the numerical solving of the sequential evolving of phase-field model. Extensive experiments are carried out in order to validate and compare the algorithm both quantitatively and qualitatively. They show the advantages of our algorithm and its readily application to jacquard texture.

Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. The target optimization problem involved can be solved efficiently with eigenvalue decomposition. Experimental results on several datasets demonstrate the effectiveness of our method.

Locality sensitive discriminant analysis (LSDA) algorithm is a new data analysis tool for studying the class relationship between data points, which can utilize local geometry structure of the data manifold and discriminant information at the same time. A major disadvantage of LSDA is it that can only deal with vector data, and thus is often confronted with singularity problem. In this paper, an extension of LSDA is proposed, called two-dimensional locality sensitive discriminant analysis (2DLSDA), which is directly based on 2D image matrices for face recognition, can overcome the singularity problem and utilize the spatial information among pixels more effectively. Besides, based on the Schur decomposition, the projection matrices can be obtained efficiently with high numerical stability, and orthogonality of projection matrix is guaranteed. Experiments on both ORL and Yale datasets demonstrate that the proposed method can achieve better performance than PCA, LDA and LSDA methods.

Dimension reduction is critical in many areas of pattern classification and machine learning and many discriminant analysis algorithms have been proposed. In this paper, a Pairwise Covariance-preserving Projection Method (PCPM) is proposed for dimension reduction. PCPM maximizes the class discrimination and also preserves approximately the pairwise class covariances. The optimization involved in PCPM can be solved directly by eigenvalues decomposition. Our theoretical and empirical analysis reveals the relationship between PCPM and Linear Discriminant Analysis (LDA), Sliced Average Variance Estimator (SAVE), Heteroscedastic Discriminant Analysis (HDA) and Covariance preserving Projection Method (CPM). PCPM can utilize class mean and class covariance information at the same time. Furthermore, pairwise weight scheme can be incorporated naturally with the pairwise summarization form. The proposed methods are evaluated by both synthetic and real-world datasets.

Image retouching is an iterative technology for repairing damaged areas from images. Most existing retouching algorithms are local in nature and extrapolate information from neighboring pixels into the damaged regions. In this paper, a novel retouching algorithm based on the Mumford-Shah (MS) model was proposed. First, the Mumford-Shah model was improved by imposing some explicit smooth constrains on the formation of discontinuities. Then, a sequence of functionals Gamma-convergent to the retouching model was proposed. Finally, the gradient flow equation associated to the sequence of functional was defined, and the finite difference approximation for numerical solving of the gradient flow equation was also presented. Experimental results on noisy jacquard images demonstrate the effectiveness of the proposed retouching algorithm.

Automatic pattern segmentation of jacquard images plays an important role in jacquard pattern analysis. This paper deals with the problem of low accuracy in segmentation of jacquard images under noisy environment A novel iterative relaxation algorithm based on Mumford-Shah model was proposed. In this algorithm, the Mumford-Shah model was approximated in the sense of /spl Gamma/-convergence by a sequence of discrete models defined on finite element spaces of adaptive triangulation. During each iteration, an adjustment procedure for the triangulation was enforced to characterize the essential contour structure of a jacquard pattern. A quasi-Newton algorithm was applied to find the absolute minimum of the discrete model at the current iteration. Experimental results on synthetic and jacquard images have shown the effectiveness and robustness of the algorithm.

The Mumford-Shah model has been well acknowledged as an important method for image segmentation. This paper discussed the problem of simultaneous image segmentation and smoothing by approaching the Mumford-Shah paradigm from a numerical approximation perspective. In particular, a novel iterative relaxation algorithm for the numerical solving of the Mumford-Shah model was proposed. First, the paper presented mathematically the existence of a solution in the weak formulation of GSBV space. Second, some approximations and numerical methods for computing the weak solution were discussed. Finally, a minimization method based on a quasi-Newton algorithm was put forward. The proposed algorithm found accurately the absolute minimum of the functional at each iteration. Considering the important role of a discrete finite element approximation method in the sense of Γ-convergence, an adjustment scheme for adaptive triangulation was applied to improve the efficiency of iteration. Experimental results on noisy synthetic and jacquard images demonstrate the efficacy of the proposed algorithm.