Recently, many compression algorithms are applied to decrease the cost of video storage and transmission. This will introduce undesirable artifacts, which severely degrade visual quality. Therefore, Video Compression ...
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Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what...
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We propose an extension of RBF networks which includes a mechanism for optimizing the complexity of the network. The approach involves two procedures: adaptation (training) and selection. The first procedure adaptivel...
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Texture feature extraction plays an important role in texture image classification. In this paper, we have proposed a texture feature extraction method by utilizing the Short-time Fourier Transform to provide local im...
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ISBN:
(纸本)9781479906505
Texture feature extraction plays an important role in texture image classification. In this paper, we have proposed a texture feature extraction method by utilizing the Short-time Fourier Transform to provide local image information, and for the global geometric correspondence we have proposed to use Spatial Pyramid Matching in frequency domain named as Short-time Fourier Transform with Spatial Pyramid Matching (STFT-SPM). The experiments are conducted on standard benchmark datasets for texture classification like Brodatz and KTH-TIPS2-a, shows that STFT-SPM can achieve significant improvement compared to the Local Phase Quantization, Weber local Descriptor and local Binary pattern methods.
A new restoration method for joint blurred images with partially known information is proposed in this paper. The joint blur here is assumed to be motion blurs and defocus blur mixed together. Under the condition of t...
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A new restoration method for joint blurred images with partially known information is proposed in this paper. The joint blur here is assumed to be motion blurs and defocus blur mixed together. Under the condition of two blur effects are supposed to be independent linear shift-invariant processes and motion blur parameter can be obtained with known information, a reduced update Kalman filter (RUKF) is used for degraded image restoration and the best defocus point spread function (PSF) parameter is determined based on the maximum entropy principle (MEP). Experimental results with real images show that the proposed approach works well.
The framework of a second order morphology algorithm is proposed to enhance the dim small infrared target in sea clutter background with strong detector noise. First, a morphological filters bank is given. Each filter...
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In this paper, we present a novel method on image calibration, utilizing Total Least Square (TLS) method and Feed-forward Neural Network, to solve the aberration problem of LAMOST two-dimensional astronomical spectral...
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ISBN:
(纸本)9781509061839
In this paper, we present a novel method on image calibration, utilizing Total Least Square (TLS) method and Feed-forward Neural Network, to solve the aberration problem of LAMOST two-dimensional astronomical spectral images. In our method, training sample set is generated with domain knowledge, from which a number of discrete points are are extracted from spectral images with fiber tracing method, and output vectors are formed by the corresponding calibrated points, obtained by utilizing the TLS method. The Feed-forward Neural Network is trained to obtain the transformation matrix, casting about for the matching relationship between the input and output sets. We also perform comparative experiments on fiber tracing and spectrum extraction results between calibrated spectral images and uncalibrated spectral images, the results show an advantage of higher accuracy and precision by our proposed method.
Simulating and rendering realistic ocean is always one of the most popular and difficult tasks in computer graphics and oceanography. However, because of the restriction of computer software and hardware conditions, m...
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Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in mo...
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ISBN:
(纸本)9781509009107
Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.
Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than singl...
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ISBN:
(纸本)9789898425843
Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than single parameter estimation. In this paper, KLIM-L covariance matrix estimation is derived theoretically based on MDL (minimum description length) principle for the small sample problem with high dimension. KLIM-L is a generalization of KLIM (Kullback-Leibler information measure) which considers the local difference in each dimension. Under the framework of MDL principle, multi-regularization parameters are selected by the criterion of minimization the KL divergence and estimated simply and directly by point estimation which is approximated by two-order Taylor expansion. It costs less computation time to estimate the multi-regularization parameters in KLIM-L than in RDA (regularized discriminant analysis) and in LOOC (leave-one-out covariance matrix estimate) where cross validation technique is adopted. And higher classification accuracy is achieved by the proposed KLIM-L estimator in experiment.
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