In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the infor...
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In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the clonal selection algorithm (CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.
This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighbor...
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This paper aims at the combination of the artificial immune network and the support vector domain description for clustering. A new artificial immune antibody network is proposed. In the network, the antibody neighborhood is represented as a support vector hypersphere, and an adaptive learning coefficient is presented. The input data set is firstly divided into subsets by antibodies, then each subset is mapped into a hypersphere respectively in a high dimensional feature space by support vector domain description. Finally the clustering results of the local support vector hyperspheres are combined to yield a global clustering solution by the minimal spanning tree, which need not a predefined number of clustering. The experimental results with several data sets illustrate the effectiveness of the proposed algorithm.
To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrö...
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To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.
A transient, six-cylinder diesel engine model for cold test has been developed for analyzing the engine fault through the engine torque curve. The model is based on physically working cycle, thermodynamic theory and d...
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A transient, six-cylinder diesel engine model for cold test has been developed for analyzing the engine fault through the engine torque curve. The model is based on physically working cycle, thermodynamic theory and dynamics mechanism. The simulation of this model, implemented on Matlab/Simulink, can not only achieve engine faults detection before hot test, but also indicate different causes of engine faults, such as initial phase change, intake valve closing-time delay, and so on. It is shown that the diesel engine model for cold test proves its significance to improving cold test technology.
To control the mobile robot with the surrounding information is the essential method to realize the intelligence and automatic moving. The vision information is the most important way to perceive the environment for t...
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To control the mobile robot with the surrounding information is the essential method to realize the intelligence and automatic moving. The vision information is the most important way to perceive the environment for the mobile robot. This paper presents an essential camera calibration technique for mobile robot, which is based on Pioneer II experiment platform. The technique includes transformation of coordinates system for vision system, the model and principle of image formation, camera distortion calibration. Because of non-linear distortion of camera, algorithm with optimizing operators is presented to improve calibration precision. We verify the validity and feasibility of the algorithm through experiment.
In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filt...
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In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filter. The experimental results validate the proposed method perform well over a wide range of restoration image ringing levels assessment. And the proposed model has given good agreement with observer ratings obtained in subjective experiments.
Document binarization is an active research area for many years. There are many difficulties associated with satisfactory binarization of document images and especially in cases of degraded historical documents. In th...
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ISBN:
(纸本)9781424421749
Document binarization is an active research area for many years. There are many difficulties associated with satisfactory binarization of document images and especially in cases of degraded historical documents. In this paper, we try to answer the question ldquohow well an existing binarization algorithm can binarize a degraded document image?rdquo We propose a new technique for the validation of document binarization algorithms. Our method is simple in its implementation and can be performed on any binarization algorithm since it doesnpsilat require anything more than the binarization stage. Then we apply the proposed technique to 30 existing binarization algorithms. Experimental results and conclusions are presented.
Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new appr...
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Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new approach brings in prior information at the proper time, updates scan path dynamically, needs less computational resources and reduces the probability to direct the attention to a less-meaning area. The application to search an airport target in remote sensing image was provided, through which the novel mechanism that how visual attention chose the area was described. Compared with another two region extraction models, experimental results confirm the effectiveness of the approach proposed in this paper.
This paper presents an efficient face segmentation approach based on face attention model and seeded region merging.A face attention model that jointly exploits the information of skin color and eye's position is ...
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This paper presents an efficient face segmentation approach based on face attention model and seeded region merging.A face attention model that jointly exploits the information of skin color and eye's position is first constructed to obtain a facial saliency map,which indicates the position of possible faces and is used to determine seed *** a seeded region merging algorithm based on regional facial saliency is proposed to generate a sequence of regions,and the region with the highest regional facial saliency is selected to represent each *** results on a variety of images demonstrate the good segmentation performance of the proposed face segmentation algorithm.
Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for imageprocessing and succ...
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Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural networks and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for imageprocessing and successfully employed in image fusion. However, in most PCNN-based fusion algorithms, only single pixel value is input to motivate PCNN neuron. This is not effective enough because humans are often sensitive to features, not only pixel value. In this paper, novel orientation information is considered as features to motivate PCNN. Visual observation and objective performance evaluation criteria demonstrate that the proposed algorithm outperforms typical wavelet-based, lapacian pyramid transform-based and PCNN-based fusion algorithms.
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