52867268
On Practical Stability and Stabilization of Hybrid and Switched Systems
In this paper, practical stability and stabilization problems for hybrid and switched systems are studied. We formally introduce the notions of e-practical stability and practical stabilizability. The main results of the paper include a direct method for the e-practical stability analysis of hybrid systems and sufficient conditions for the practical stabilizability of switched systems. Moreover, we construct an e-practically stabilizing switching law in the proof of the stabilizability results and apply it to a tracking problem to show its effectiveness.
31360769
Results and Perspectives on Computational Methods for Optimal Control of Switched Systems
This paper surveys some of the recent progresses on computational methods for optimal control of switched systems. A general model of switched system that allows externally forced switching (EFS) and internally forced switching (IFS) is first introduced and two important classes of optimal control problems are formulated. After a brief review of some relevant theoretical results, we present the idea of two stage optimization. Based on the theoretical results and two stage optimization, we then survey computational methods based on discretization, and computational methods not based on discretization. Comments are made on the merits and restrictions of each method.
17705335
Active Morphable Model: an efficient method for face analysis
Multidimensional Morphable Model is a powerful model to analyze and synthesize human faces. However, the stochastic gradient descent algorithm adopted to match the Morphable Model to a novel face image is not efficient enough. In this paper, a very efficient optimization method devised for Morphable Model matching is proposed, called Active Morphable Model (AMM). The kernel of AMM is an iterative algorithm directly utilizing the heuristic information provided by the novel image, and updating the model parameters in a computationally economic fashion. AMM is more efficient than general optimization methods in matching a Morphable Model, it has much higher convergent rate and matching speed. Furthermore, it is insensitive to the initial estimation of the face pose, and is robust when used to match novel faces with large variations in translation, rotation and scaling. Experimental results are given to validate the efficiency and robustness of the proposed method.
14912672
Face recognition with MRC-boosting
In this paper, a novel classification algorithm called MRC-Boosting is proposed. Through aggregating maximal-rejection-classifier features under boosting framework, this algorithm can deal with complicated two-class classification problem, especially for the category called target detection problem where a target class should be discriminated from tile surrounding clutter class. MRC-Boosting is efficient since unlike many other boosting based algorithms, at each iteration the optimal feature is computed in closed-form, with neither exhaustive search nor time-consuming numerical optimization. Furthermore, a variant of MRC-Boosting is derived and applied to face recognition. This variant MRC-Boosting algorithm is able to utilize large amount of training samples efficiently overcoming the difficulty faced by other algorithms like AdaBoost. The effectiveness of the proposed algorithm is validated by face recognition experiments on CMU-PIE database
14782191
Entropy optimized contrast stretch to enhance remote sensing imagery
This paper presents a contrast stretch (CS) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (GPR), synthetic aperture radar, and infra-red imagery. The CS enhances contrast of the low-contrast part of an image. In remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. The CS modifies the original image such that pixel values above and below preset boundaries are set to Zero and the maximum possible pixel value and the pixel values falling between the boundaries are stretched out to enhance the contrast of the image. Using the CS we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. On the other hand, the CS inevitably enhances other parts of a remote sensing images, such as clutter and measurement noise. Therefore there is a trade-off in using the CS. It is beneficial to find the correct "cut-off" boundaries in the CS in some optimal sense. We propose using minimum entropy as a criterion of looking for the optimal CS parameter Using field data from GPR application, we show that improved image can be obtained which makes further processing such as detection more accurate.
12620902
Adaptive two-pass median filter to remove impulsive noise
In this paper, we present an adaptive two-pass median filter to remove impulsive noise. In two-pass median filtering, an image contaminated by impulsive noise is processed by a median filter twice. Median filtering is a non-reversible process, i.e., useful information discarded by the filter cannot be recovered. This behavior becomes more apparent in two-pass median filtering. To correct this problem, between the two filtering processes we introduce an adaptive process to selectively replace some pixels by their original values based on the spatial distribution of estimated impulsive noise. Compared with standard median filtering and two-pass median filtering, better results are obtained in terms of visual appreciation and mean squared error. We use examples to demonstrate the performance of the method.