The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circ...
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The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fastalgorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.
Extreme learning machine (ELM) proposed by Huang et al. was developed for generalized single hidden layer feedforward networks (SLFNs) with a wide variety of hidden nodes. It proved to be very fast and effective espec...
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Extreme learning machine (ELM) proposed by Huang et al. was developed for generalized single hidden layer feedforward networks (SLFNs) with a wide variety of hidden nodes. It proved to be very fast and effective especially for solving function approximation problems with a predetermined network structure. However, the method for determining the network structure of preliminary ELM may be tedious and may not lead to a parsimonious solution. In this paper, a systematic two-stage algorithm (named TS-ELM) is introduced to handle the problem. In the first stage, a forward recursivealgorithm is applied to select the hidden nodes from the candidates randomly generated in each step and add them to the network until the stopping criterion achieves its minimum. The significance of each hidden node is then reviewed in the second stage and the insignificance ones are removed from the network, which drastically reduces the network complexity. The effectiveness of TS-ELM is verified by the empirical studies in this paper. (C) 2010 Elsevier B.V. All rights reserved.
A fast recursive algorithm for the vertical Bell Laboratories layered space-time (V-BLAST) with the optimal ordered successive interference cancellation (SIC) detection has been proposed by Benesty-Huang-Chen and two ...
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A fast recursive algorithm for the vertical Bell Laboratories layered space-time (V-BLAST) with the optimal ordered successive interference cancellation (SIC) detection has been proposed by Benesty-Huang-Chen and two improved algorithms have been also recently introduced by Szczecinski-Massicotte and Zhu-Lei-Chin independently. In this letter, our first contribution is to incorporate the existing improvements into the original fast recursive algorithm to provide the fastest known algorithm for the optimal ordered SIC detection of V-BLAST. Subsequently, we propose two new algorithms that result from one further improvement for the fast recursive algorithm. Compared with the fastest known algorithm built from the existing improvements, one new proposed algorithm has a speedup of 1.3 times in both the number of multiplications and the number of additions, and the other new proposed algorithm requires less intermediate variables and saves memories.
Due to computational complexity of correlation searching, the digital image correlation (DIC) method is often extremely time consuming in image processing and optical measurement, which has limited its further applica...
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Due to computational complexity of correlation searching, the digital image correlation (DIC) method is often extremely time consuming in image processing and optical measurement, which has limited its further applications to a great extent. This paper develops a fastrecursive scheme to mathematically reduce the computational burden of the traditional DIC technique and therefore improve its efficiency in deformation calculation. A global sum-table approach is proposed to simplify the computations of all double sums arising in the zero-normalized cross-correlation coefficient (ZNCC). A fast recursive algorithm is established to accelerate the calculation of the cross-correlation term in the ZNCC. Both theoretical analysis and actual displacement acquisition are carried out to validate the performance of the new DIC algorithm, which indicates that the fastrecursive scheme can improve computational efficiency of integer-pixel correlation searching by about 10 to 50 times in comparison with the classic DIC algorithm, on the condition of keeping the measurement accuracy.
Most control strategies for networked control system (NCS) assume that system model is known as 'a priori', which are however impractical in many industrial applications. To obtain a suitable model for network...
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Most control strategies for networked control system (NCS) assume that system model is known as 'a priori', which are however impractical in many industrial applications. To obtain a suitable model for networked control, this paper concentrates on model identification under the networked control environment. A networked identification scheme is proposed here, and nondeterministic factors such as network-induced delay, data packet out-of-order, and data packet loss between the sensor and identifier as well as the identifier and the actuator are considered. A discard-packet strategy is first developed to deal with network nondeterministic factors from the sensor to the identifier, and a cubic spline interpolation is used to compensate lost data. Actuator buffers are then introduced for actuator nodes to handle network nondeterministic factors between the identifier and the actuator. Finally, a fast recursive algorithm is used both to select the model structure and to estimate the unknown system parameters. Experiments on ARMA model identification and NARMAX model identification are performed respectively under different network loads. Simulation results show that the proposed networked identification scheme can effectively overcome the impact of various network-induced nondeterministic factors and significantly improve model identification performance. (C) 2008 Elsevier Inc. All rights reserved.
The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. U...
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The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
In this paper, a structure-adaptive approach to the nonlinear image filtering is described. The adaptive procedure is based on selection of the most homogenous neighborhood region from several possible structuring reg...
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ISBN:
(纸本)0819424374
In this paper, a structure-adaptive approach to the nonlinear image filtering is described. The adaptive procedure is based on selection of the most homogenous neighborhood region from several possible structuring regions by the principle of maximum a posteriori probability. Then, an optimal evaluation of the pixel value is performed involving pixels from the determined neighborhood region. Trimmed mean filters are used for the robust evaluation of local properties during estimation of object and background intensities when the noise has a mixed conditional distribution, e.g. normal distribution with outliers.
This paper considers the construction of approximants of multi-input-multi-output, discrete-time linear systems from the finite data of the impulse response and autocorrelation sequences. In the approximation of a mul...
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This paper considers the construction of approximants of multi-input-multi-output, discrete-time linear systems from the finite data of the impulse response and autocorrelation sequences. In the approximation of a multivariable linear system, it is common practice to use a finite portion of its impulse response sequence. This is formally equivalent to the Padé approximation technique, which may produce unstable approximants, even though the original system is stable. Mullis and Roberts proposed a new method, which yields stable approximants, in connection with approximation of digital filters. This is, however, restricted to the single-input-single-output case. This paper extends their method to the multi-input-multi-output case and shows a fast recursive algorithm to construct stable approximants of linear systems.
This paper considers the construction of approximants of multi-input-output, discrete-time linear systems from the finite data of the impluse response and autocorrelation sequences (or cross correlation between input ...
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This paper considers the construction of approximants of multi-input-output, discrete-time linear systems from the finite data of the impluse response and autocorrelation sequences (or cross correlation between input and output, and autocorrelation of output). In the approximation of multivariable linear systems, it is common practice to use the impluse response sequence This is formally equivalent to the Padé approximation technique, which may produce unstable approximants, even though the original system is stable. Mullis and Roberts proposed a new method, which yields stable appr0ximants, in connection with the approximation of digital filters. This is, however, restricted to the single input-output case. This paper extends their method to the multi-input-output case and shows a fast recursive algorithm to construct stable approximants of linear systems
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