Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithmby using only local phase information. We also demonstrate that local...
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Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithmby using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector;it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm.
A new construction adder based on Chinese abacus algorithm is presented in this paper. There are two kinds of beads used in this construction. Each column element has three higher beads with a weight of four and three...
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A new construction adder based on Chinese abacus algorithm is presented in this paper. There are two kinds of beads used in this construction. Each column element has three higher beads with a weight of four and three lower beads with a weight of one. The proposed 32-bit adder contains eight column elements. The construction was simulated by the technology of TSMC 0.18 mu m CMOS process. Layout was also made by the same technology. The maximum delay of the 32-bit abacus adder is 0.91 ns and 14% less than that of Carry Look-ahead Adders for 0.18 mu m technology. The power consumption of the abacus adder is 3.1 mW and 28% less than that of Carry Look-ahead Adders for 0.18 mu m technology. Recent researches are compared with the proposed adder. The construction was also simulated by Predictive Technology Model. The PTM results also presented. The use of Chinese abacus approach offers a competitive technique with respect to other adders. (C) 2012 Elsevier Ltd. All rights reserved.
Modeling and simulation is essential for performance evaluation of wireless systems and specific algorithms used, for instance, during resource allocation. For Current and future wireless systems, several properties h...
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Modeling and simulation is essential for performance evaluation of wireless systems and specific algorithms used, for instance, during resource allocation. For Current and future wireless systems, several properties have to be considered such as data traffic, frequency selective fading channels, and radio resource management. Throughout this article, a new system-level simulator concept is presented for packet switched systems using OFDMA, which is called snapshot-based SLS;and represents a compromise between state-of-the-art static and dynamic SLSs. The proposed methodology only considers short time intervals within the busy hour by their statistics so that RRM and data traffic can be considered, as in dynamic SLS, but with lower computational complexity. In this article a description is given of how the cellular setup and traffic generation are performed for the proposed snapshot concept. Furthermore, a new methodology is proposed for a quality measure of resource units that is applicable to future wireless systems using an interleaved subcarrier allocation. Exemplary simulation results show that the developed concept is able to evaluate the performance of OFDMA systems considering the impact of, for example, data traffic and resource allocation strategies.
A feature extraction algorithm for palm-print recognition based on two dimensional discrete wavelet transform is proposed in this paper, which efficiently exploits the local spatial variations in a palm-print image. T...
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A feature extraction algorithm for palm-print recognition based on two dimensional discrete wavelet transform is proposed in this paper, which efficiently exploits the local spatial variations in a palm-print image. The palm-image is segmented into several spatial modules and a palm-print recognition scheme is developed, which extracts histogram-based dominant wavelet features from each of these local modules. The effect of modularization in terms of the entropy content of the palm-print images has been analyzed. The selection of dominant features for the purpose of recognition not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, the modularization of the palm-print image enhances the discriminating capabilities of the proposed features and thereby results in high within-class compactness and between-class separability of the extracted features. Different types of Daubechies wavelets (in terms of use of number of vanishing moments, i.e., db1-db10) have been utilized for the purpose of feature extraction and the effect upon the recognition performance has been also investigated. In order to further reduce the feature dimension, a principal component analysis is performed. It is found from our extensive experimentations on different palm-print databases that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods. (C) 2013 Elsevier Ltd. All rights reserved.
We proposed a recovery scheme for image deblurring. The scheme is under the framework of sparse representation and it has three main contributions. Firstly, considering the sparse property of natural image, the nonloc...
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We proposed a recovery scheme for image deblurring. The scheme is under the framework of sparse representation and it has three main contributions. Firstly, considering the sparse property of natural image, the nonlocal overcompleted dictionaries are learned for image patches in our scheme. And, then, we coded the patches in each nonlocal clustering with the corresponding learned dictionary to recover the whole latent image. In addition, for some practical applications, we also proposed a method to evaluate the blur kernel to make the algorithm usable in blind image recovery. The experimental results demonstrated that the proposed scheme is competitive with some current state-of-the-art methods.
Relying on the possibility of generating a second-order sliding motion by using, as control, the first derivative of the control signal instead of the actual control, a new solution to the problem of chattering elimin...
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Relying on the possibility of generating a second-order sliding motion by using, as control, the first derivative of the control signal instead of the actual control, a new solution to the problem of chattering elimination in variable structure control (VSC) is presented. Such a solution, inspired by the classical bang-bang optimal control strategy, is first depicted and expressed in terms of a control algorithm by introducing a suitable auxiliary problem involving a second-order uncertain system with unavailable velocity. Then, the applicability of the algorithm is extended, via suitable modifications, to the case of nonlinear systems with uncertainties of more general types. The proposed algorithm does not require the use of observers and differential inequalities and can be applied in practice by exploiting such commercial components as peak detectors or other approximated methods to evaluate the change of the sign of the derivative of the quantity accounting for the distance to the sliding manifold.
As columns age and differ between systems, retention times for comprehensive two-dimensional gas chromatography (GCxGC) may vary between runs. To properly analyze GCxGC chromatograms, it often is desirable to align th...
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As columns age and differ between systems, retention times for comprehensive two-dimensional gas chromatography (GCxGC) may vary between runs. To properly analyze GCxGC chromatograms, it often is desirable to align the retention times of chromatographic features, such as analyte peaks, between chromatograms. Previous work by the authors has shown that global, low-degree polynomial transformation functions, namely affine, second-degree polynomial, and third-degree polynomial, are effective for aligning pairs of two-dimensional chromatograms acquired with dual second columns and detectors (GCx2GC). This work assesses the experimental performance of these global methods on more general GCxGC chromatogram pairs and compares their performance to that of a recent, robust, local alignment algorithm for GCxGC data [Gros et al. Anal. Chem. 2012, 84, 9033]. Measuring performance with the root-mean-square (RMS) residual differences in retention times for matched peaks suggests that global, low-degree polynomial transformations outperform the local algorithm given a sufficiently large set of alignment points, and are able to improve misalignment by over 95% based on a lower-bound benchmark of inherent variability. However, with small sets of alignment points, the local method demonstrated lower error rates (although with greater computational overhead). For GCxGC chromatogram pairs with only slight initial misalignment, none of the global or local methods performed well. In some cases with initial-misalignment near the inherent variability of the system, these methods worsened alignment, suggesting that it may be better not to perform alignment in such cases.
The goal of this paper is to present a novel chaotic particle swarm optimization (CPSO) algorithm and compares the efficiency of three one-dimensional chaotic maps within symmetrical region for long-term cascaded hydr...
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The goal of this paper is to present a novel chaotic particle swarm optimization (CPSO) algorithm and compares the efficiency of three one-dimensional chaotic maps within symmetrical region for long-term cascaded hydroelectric system scheduling. The introduced chaotic maps improve the global optimal capability of CPSO algorithm. Moreover, a piecewise linear interpolation function is employed to transform all constraints into restrict upriver water level for implementing the maximum of objective function. Numerical results and comparisons demonstrate the effect and speed of different algorithms on a practical hydro-system. (C) 2009 Elsevier Ltd. All rights reserved.
The main services in cloud computing are SaaS (Software as a Service), PaaS (Platform as a Service) and laaS (Infrastructure as a Service). Among these services, server virtualization falls into laaS which is a servic...
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The main services in cloud computing are SaaS (Software as a Service), PaaS (Platform as a Service) and laaS (Infrastructure as a Service). Among these services, server virtualization falls into laaS which is a service for lowering server maintenance costs. Generally, the primary aim of server virtualization has been to improve system performance by replacing multiple servers with a single server with better performance. But it carries the problem of increased cost, needed for the installation of new servers for server virtualization. In this paper, grid infrastructure is used for server virtualization in which existing servers are used rather than bringing in new servers. Server virtualization service is provided using scheduling algorithms for distributed servers or resources in grid computing. To overcome potential performance limitations that come from using existing servers, mathematical models of Meta and Sleep Servers under the grid infrastructure environment are used to provide server virtualization service with high availability. (C) 2012 Elsevier Ltd. All rights reserved.
Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few lo...
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Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.
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