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An improved convex programming model for the inverse problem in intensity-modulated radiation therapy

International Journal of Performability Engineering 2018年第5期14卷 871-884页

作者： Lan, Yihua Zhang, Xingang Zhang, Jianyang Wang, Yang Hung, Chih-Cheng School of Computer and Information Technology Nanyang Normal University Nanyang473061 China Institute of Image Processing and Pattern Recognition Nanyang Normal University Nanyang473061 China Radiology Department Central Hospital of Nanyang Nanyang473061 China Laboratory for Machine Vision and Security Research College of Computing and Software Engineering Kennesaw State University - Marietta Campus 1100 South Marietta Parkway MariettaGA30067-2896 United States

Intensity modulated radiation therapy technology (IMRT) is one of the main approaches in cancer treatment because it can guarantee the killing of cancer cells while optimally protecting normal tissue from complications. Inverse planning, which is the core component of the entire IMRT system, is mainly based on accurate mathematical modeling and associated fast solving methods. In inverse planning, the fluence map optimization, which considers the multi-leaf collimator (MLC) modulation, is the current research focus. Although the hitting constrain problem with the unidirectional movement of leaf-sweeping has been solved, our goal is to solve the hitting constrain problem with the bidirectional movement of leaf-sweeping. In this study, we propose a non-synchronized type to solve the hitting constrain problem with the bidirectional movement of leaf-sweeping schemes for IMRT. In solving this problem, a new mathematical model is proposed under the framework of convex programming. The advantage of the convex model is to avoid the uncertainty and inaccuracy that occurs in the non-convex programming solving process. Experimental results for two clinical testing cases show that under the same condition of total number of monitoring units, the new proposed model produces better dose distribution than those of the total variance and quadratic models. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Convex optimization

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Combining LPP with PCA for microarray data clustering

Combining LPP with PCA for microarray data clustering

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Congress on Evolutionary Computation

作者： Chuanliang Chen Rongfang Bie Ping Guo Department of Computer Science Beijing Normal University Beijing China Processing & Pattern Recognition Laboratory Beijing Normal University Beijing China

DNA microarray technique has produced large amount of gene expression data. To analyze these data, many excellent machine learning techniques have been proposed in recent related work. In this paper, we try to perform the clustering of microarray data by combining the recently proposed locality preserving projection (LPP) method with PCA, i.e. PCA-LPP. The comparison between PCA and PCA-LPP is performed based on two clustering algorithms, K-means and agglomerative hierarchical clustering. As we already known, clustering with the components extracted by PCA instead of the original variables does improve cluster quality. Moreover, our empirical study shows that by using LPP to perform further process the dimensions of components extracted by PCA can be further reduced and the quality of the clusters can be improved greatly meanwhile. Particularly, the first few components obtained by PCA-LPP capture more information of the cluster structure than those of PCA.

关键词： Evolutionary computation

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RBF Network image Representation with Application to CT image Reconstruction

RBF Network image Representation with Application to CT Imag...

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International Conference on Computational Intelligence and Security

作者： Ping Guo Ming Hu Yunde Jia Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science Beijing Institute of Technology Beijing China Beijing Normal University Beijing Beijing CN China

ISBN: (纸本)1424406048

Radial basis function (RBF) neural network can be used as a universal approximator. In this paper, we propose a novel method to apply RBF net to reconstruct 2-dimensional computerized tomography (CT) images from a small amount of projection data. In the method, the cross-sectional image is represented by a RBF network, the unknown cross-sectional image vector is replaced by the function of the network's weight vector. As proved by us, the line integral of the weight matrix can be calculated providing the projections of the CT image are known. The ART method can be employed to obtain the final reconstructed CT image. Experiments show that the proposed method can obtain the better reconstructed image than the filtered back projection (FBP), and it is also more efficient than ART method alone

关键词： Radial basis function networks image representation Computed tomography image reconstruction Subspace constraints Matrix converters Equations Application software Laboratories Neural networks

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A New Deep Fuzzy Based MSER Model for Multiple Document images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： image classification

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot 5

Gait Planning and Motion Control Based on Vrep Simulation fo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhou, Linqi Chen, Zhihua Liu, Jun Liu, Zhi Chen, Yumeng Zhang, Liting Nanchang Hangkong University Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Moe Key Lab of Nondestructive Testing Technology Nanchang330063 China Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing100081 China

ISBN: (纸本)9798350307320

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot's foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper. © 2023 IEEE.

关键词： Robot programming

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Blind source separation with evolution based KICA

Blind source separation with evolution based KICA

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781479979684

Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.

关键词： Kernel Independent component analysis Algorithm design and analysis Multimedia communication Correlation Optimization Asia

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An Algorithm for Calculating the Hypervolume Contribution of a Set

An Algorithm for Calculating the Hypervolume Contribution of...

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World Automation Congress

作者： Xiuling Zhou Ping Guo C. L. Philip Chen Image Processing and Pattern Recognition Lab Beijing Normal University Beijing 100875 China The Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (纸本)9781467344975

The reliability model can be optimized with a multi-objective optimization algorithm, while hypervolume-based multi-objective evolutionary algorithms (MOEAs) have been shown to produce better results for multi-objective problem in practice. When hypervolume is used in some MOEAs as archiving strategy, diversity mechanism or selection criterion to guide the search, it is necessary to determine which subset contributes the least hypervolume contribution. Few algorithms have been designed for this purpose. In this paper a new algorithm based on HSO (hypervolume by slicing objective) is proposed for calculating the exclusive hypervolume contributions of each subset to the whole nondominated set directly for small dimension. The new algorithm is composed of two parts: the algorithm SHSO (set hypervolume contribution by slicing objective) and the algorithm SHSO*. SHSO is used to calculate the exclusive hypervolume contribution of a subset to the whole nondominated set. SHSO* is applied to select the subset which contributes the least hypervolume contribution by repeated application of SHSO. Compared with HSO adopted for calculating the exclusive hypervolume contribution, SHSO* outperforms HSO for all of the test fronts with small dimension.

关键词： Keyword - Reliability model Multi-objective evolutionary algorithms Hypervolume contribution

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Voronoi-Based Sleeping Configuration in Wireless Sensor Networks with Location Error

Voronoi-Based Sleeping Configuration in Wireless Sensor Netw...

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IEEE International Conference on Networking, Sensing and Control

作者： Xinyu Chen Michael R. Lyu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Department of Computer Science and Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In this paper, we present a Voronoi-based sleeping configuration to deal with different sensing radii and location error. With our proposed sleeping candidate condition, redundant sensors are optionally identified and scheduled to sleep in order to extend the system lifetime while maintaining adequate sensor redundancy to tolerate sensor failures, energy depletions, and location error. Simulation results show that there is a tradeoff among energy conservation, area coverage, and fault tolerance, which varies between different sleeping candidate conditions.

关键词： Wireless sensor networks Sensor systems Surveillance Quality of service Redundancy Energy conservation Fault tolerance Sensor phenomena and characterization Batteries image processing

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Discovering structural regularity in facade image

Discovering structural regularity in facade image

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IEEE International Conference on Systems, Man and Cybernetics

作者： Weiwei Li Xin Zheng Jie Chen Image Processing and Pattern Recognition College of Information Science and Technology Beijing Normal University Beijing China Information Center CITIC Guoan Group Beijing China

3D reconstruction from single view is a very difficult problem in computer vision and computer graphics. Few works have been done in this area. In this paper, we proposed a method to discover structural regularity in single facade images on the procedural modeling of buildings by using SIFT algorithm. Firstly, symmetry information is detected from the image using SIFT algorithm for image analysis. Next, the symmetry information will be removed from the facade image. Finally, get the SIFT feature of widows and doors. Experiments show that our method can successfully discover regular structures with a higher recognition rate than existing method especially amidst shadowing and the changing illumination environment.

关键词： Laboratories

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An efficient and scalable learning algorithm for Near-Earth objects detection in astronomy big image data

An efficient and scalable learning algorithm for Near-Earth ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ke Wang Ping Guo School of computer Science and Technology Beijing Institute of Technology Beijing P.R. China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing P.R. China

ISBN: (纸本)9781479938414

In this paper, we investigate the efficiency and scalability of Gaussian mixture model based learning algorithm for the detection of Near-Earth objects in large scale astronomy image data. We propose an effective scheme to reduce the computational complexity of current learning algorithm, this is achieved by adopting the perceptual image hashing method. Our proposed scheme is validated on raw astronomy image data. The experiment results illustrate that both efficiency and scalability are improved significantly in astronomical scenario and other scenario.

关键词： Astronomy Scalability Big data Data mining Gaussian distribution Hamming distance Gaussian mixture model

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