Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issu...
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ISBN:
(纸本)9781509006212
Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model is a more difficult problem if one has not sufficient experience. To deal with this problem, an evolution based method to select the kernel model of KICA is proposed in this paper. There are two main features of the proposed method: one is that using a multiple kernel model, a convex combination of several single kernels, replaces the single kernel model;another is that particle swarm optimization (PSO) algorithm is utilized to find the combination weights of the composite kernel. Experiments conducted on separating one-dimensional mixed signals, nature images, and spectroscopic CCD images showed that using multiple kernels model with PSO kernel selection algorithm can enhance the performance of KICA.
image segmentation is the basis of imageprocessing and image analysis. However, there are no common method that can be used in natural images, and present methods fail to explain understandings of human's visual ...
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image segmentation is the basis of imageprocessing and image analysis. However, there are no common method that can be used in natural images, and present methods fail to explain understandings of human's visual system. In this paper, we propose to apply Karklin's visual perception model to extract feature vectors of images, and the features are clustered with K-means method. The results obtained in feature space are projected back to the image space to finish segmentation. A comparison with the Normalized Cuts (Ncut) method is done, and it turns out that proposed method outperform Ncut in texture rich images.
At present, multiple scattering problems in participating media is still very challenging for real time rendering. Some methods have proposed to describe multiple scattering phenomena, however, there are some restrict...
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ISBN:
(纸本)9781467317139
At present, multiple scattering problems in participating media is still very challenging for real time rendering. Some methods have proposed to describe multiple scattering phenomena, however, there are some restriction conditions such as requiring the medium is static, etc., and rendering speed is not satisfied real-time requirement. In order to speed up the multiple scattering rendering, we propose a GPU based algorithm in this paper. First of all, the media is initialized with a particle system and the property of each particle is defined;secondly, according to the properties of each particle, a method of tracing the particle path, which is generated by uniformly sampling the surrounding particles of one particle, is proposed and this method is used to compute in-scattering radiance for each particle;finally, the total radiance is calculated by summing up contributions of particles along ray paths and the final image is rendered. The experimental results show that the proposed algorithm can achieve the real-time rendering effect.
A new edge detection operator based on image feature is proposed,which analyze edges in image for edge feature in two *** local extreme of the operator is created at the edge location and low value is created at the s...
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A new edge detection operator based on image feature is proposed,which analyze edges in image for edge feature in two *** local extreme of the operator is created at the edge location and low value is created at the smooth *** can be located by obtaining the local extreme and a threshold of the operator response. The detection operator is shown to be better than Canny operator in terms of signal-to-noise ratio and edge location accuracy.
Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Differenc...
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Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users' experience, which is difficult to ch...
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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 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.
Improved particle swarm optimization algorithm with harmony search (IHPSO) is proposed in this paper. This algorithm takes particle swarm search direction estimation mechanism and harmony search (HS) approach to parti...
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The research of point spread function (PSF) of astronomical object imaging is very important to the astronomical image restoration. In this paper, the simulated atmospheric turbulent phase screen, the short exposure P...
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In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foregroun...
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