18 Result(s)
within
Jianwu Li
Artificial Intelligence
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Chapter and Conference Paper
Application of CG Pseudo-spectral Method to Optimal Posture Adjustment of Robot Manipulator
To consider the energy saving during the robot motion, optimal posture control method for a robot manipulator is proposed. The Chebyshev-Gauss (CG) Pseudo-spectral method is used to discuss the problem with th...
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Chapter and Conference Paper
Quality-Aware Memory Network for Interactive Volumetric Image Segmentation
Despite recent progress of automatic medical image segmentation techniques, fully automatic results usually fail to meet the clinical use and typically require further refinement. In this work, we propose a quali...
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Article
Occluded offline handwritten Chinese character recognition using deep convolutional generative adversarial network and improved GoogLeNet
In this paper, we propose a novel method for recognizing occluded offline handwritten Chinese characters based on deep convolutional generative adversarial network (DCGAN) and improved GoogLeNet. Different fro...
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Article
Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss
Cone beam computed tomography (CBCT) is an important tool for clinical diagnosis and many industrial applications. However, ring artifacts usually appear in CBCT images, due to device responding inconsistence...
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Chapter and Conference Paper
Generating Low-Rank Textures via Generative Adversarial Network
Achieving structured low-rank representation from the original image is a challenging and significant task, owing to the capacity of the low-rank structure in expressing structured information from the real wo...
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Chapter and Conference Paper
Training Deep Autoencoder via VLC-Genetic Algorithm
Recently, both supervised and unsupervised deep learning techniques have accomplished notable results in various fields. However neural networks with back-propagation are liable to trap** at local minima. Ge...
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Chapter and Conference Paper
Face Hallucination Using Correlative Residue Compensation in a Modified Feature Space
Local linear embedding (LLE) is a promising manifold learning method in the field of machine learning. Number of face hallucination (FH) methods have been proposed due to its neighborhood preserving nature. Ho...
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Chapter and Conference Paper
Removing Ring Artifacts in CBCT Images Using Smoothing Based on Relative Total Variation
Removing ring artifacts in Cone Beam Computed Tomography (CBCT) images without impairing the image quality is critical for the application of CBCT. In this paper, we propose a novel method for the removal of r...
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Chapter and Conference Paper
Fast Dual-Tree Wavelet Composite Splitting Algorithms for Compressed Sensing MRI
We presented new reconstruction algorithms for compressed sensing magnetic resonance imaging (CS-MRI) based on the combination of the fast composite splitting algorithm (FCSA) and complex dual-tree wavelet tra...
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Article
Community detection in complex networks using extended compact genetic algorithm
Complex networks are often studied as graphs, and detecting communities in a complex network can be modeled as a seriously nonlinear optimization problem. Soft computing techniques have shown promising results...
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Chapter and Conference Paper
Credit Scoring Based on Kernel Matching Pursuit
Credit risk is paid more and more attention by financial institutions, and credit scoring has become an active research topic. This paper proposes a new credit scoring method based on kernel matching pursuit (...
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Chapter and Conference Paper
Learning KPCA for Face Recognition
Kernel principal component analysis (KPCA) is an effective method for face recognition. However, the expression of its final solution needs to take advantage of all training examples, such that its run in real...
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Chapter and Conference Paper
Constructing the Shortest ECOC for Fast Multi-classification
Error-correcting output codes (ECOC) is an effective method to perform multi-classification via decomposing a multi-classification problem into many binary classification tasks, and then integrating the output...
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Chapter and Conference Paper
Efficient Semantic Kernel-Based Text Classification Using Matching Pursuit KFDA
A number of powerful kernel-based learning machines, such as support vector machines (SVMs), kernel Fisher discriminant analysis (KFDA), have been proposed with competitive performance. However, directly apply...
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Chapter and Conference Paper
Super Resolution of Text Image by Pruning Outlier
We propose a learning based super resolution algorithm for single frame text image. The distance based candidate of example can’t avoid the outliers and the super resolution result will be disturbed by the irr...
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Chapter and Conference Paper
Constructing Sparse KFDA Using Pre-image Reconstruction
Kernel Fisher Discriminant Analysis (KFDA) improves greatly the classification accuracy of FDA via using kernel trick. However, the final solution of KFDA is expressed as an expansion of all training examples,...
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Chapter and Conference Paper
Refining Kernel Matching Pursuit
Kernel matching pursuit (KMP), as a greedy machine learning algorithm, appends iteratively functions from a kernel-based dictionary to its solution. An obvious problem is that all kernel functions in dictionar...
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Chapter and Conference Paper
Kernel Matching Reduction Algorithms for Classification
Inspired by kernel matching pursuit (KMP) and support vector machines (SVMs), we propose a novel classification algorithm: kernel matching reduction algorithm (KMRA). This method selects all training examples ...
