This paper proposes a fast and robust algorithm for classification and recognition of ships based on the Principal Component Analysis (PCA) method. The three-dimensional ship models are achieved by modeling software o...
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This paper proposes a fast and robust algorithm for classification and recognition of ships based on the Principal Component Analysis (PCA) method. The three-dimensional ship models are achieved by modeling software of MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The PCA method as against the Back-Propagation (BP) neural network method for simulated ship recognition using training and testing experiments, we can see that there is a sharp contrast between them. Some recognition results from simulated data are presented, the correct recognition rate of PCA method improved rapidly for each of the five ship types than that of neural network method, the number of times a ship type is recognized as one of the other ships is reduced greatly.
A Linear Quadratic robust control algorithm is proposed for the system with non-stochastic *** this paper,the uncertainties are the noises which lie in a bounded ellipsoid *** assumption weakens the requirements of th...
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ISBN:
(纸本)9781509046584
A Linear Quadratic robust control algorithm is proposed for the system with non-stochastic *** this paper,the uncertainties are the noises which lie in a bounded ellipsoid *** assumption weakens the requirements of the known Gaussian distribution in the traditional Linear Quadratic Gaussian（LQG） *** on the linear characteristic of the system and robust optimization theory,the linear quadratic robust controller is *** simulation results show the effectiveness of the algorithm.
A new change detection approach based on non-parametric density estimation and Markov random fields is proposed in this paper. As the concrete form of gray statistical distribution of remote sensing images is often di...
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In this paper, we take the advantages of color contrast and color distribution to get high quality saliency maps. The overall procedure flow of our unified framework contains superpixel pre-segmentation, color contras...
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C-mode imaging is one of the ultrasound imaging modalities. Compared with other modalities, e.g. A-mode, B-mode, M-mode, and Doppler, C-mode is mainly developed and used in industry testing. The potential of C-mode im...
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Facial expression recognition (FER) is challenging, when transiting from the laboratory to in-the-wild situations. In this paper, we present a general framework for the Learning from Synthetic Data Challenge in the 4t...
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A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly foc...
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A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.
In this paper,the distributed optimization problem is investigated under a second-order multi-agent *** the proposed algorithm,each agent solves the optimization via local computation and information exchange with its...
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ISBN:
(纸本)9781509046584
In this paper,the distributed optimization problem is investigated under a second-order multi-agent *** the proposed algorithm,each agent solves the optimization via local computation and information exchange with its neighbors through the communication ***,in comparison with the existing second-order distributed optimization algorithms,the proposed algorithm is much simpler due to one coupled information exchange among the agents is *** achieve the optimization,the distributed algorithm is proposed based on the consensus method and the gradient *** optimal solution of the problem is thus obtained with the design of Lyapunov function and the help of LaSallel's Invariance Principle.A numerical simulation example and comparison of proposed algorithm with existing works are presented to illustrate the effectiveness of the theoretical result.
The imaging rate of structured illumination microscopy(SIM) reached 188 Hz recently. As the exposure time decreases, the camera detects fewer virtual photons, while the noise level remains the same. As a result, the s...
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The imaging rate of structured illumination microscopy(SIM) reached 188 Hz recently. As the exposure time decreases, the camera detects fewer virtual photons, while the noise level remains the same. As a result, the signal-to-noise ratio(SNR) decreases sharply. Furthermore, the SNR decreases further because of photobleaching and phototoxicity. This decreased quality of SIM raw data may lead to surprising artifacts with various causes, which may confuse a new user of SIM microscopy. We summarize three significant possible sources of severe artifacts in reconstructed super-resolution(SR)images. Ultrafast motion of a biological sample or an uneven illumination pattern is the most difficult to be identified. The estimated parameter could also be incorrect, leading to artifact of regular ***, reconstruction with the Wiener method generates stochastic artifacts due to the amplification of noise during the deconvolution process. To deal with these problems, we have established a protocol to reconstruct ultrafast SIM raw data obtained in low SNR conditions. First, we checked the quality of the raw data with the image J plugin SIMcheck before reconstruction. Then, a modified parameter estimation method was used to improve the precision of the parameters. Finally, an iterative algorithm was used for SIM reconstruction under low signal-to-noise ratio conditions. This procedure effectively suppressed the artifacts in the super-resolution images reconstructed from raw data of low signal-to-noise ratio.
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