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International Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping

作者： Zhang, Xiaohua Wang, Le Jiao, L.C. Key Lab. of Intelligent Perception and Image Understanding of the Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424494040

In this paper, a novel approach is proposed for unsupervised change detection of multitemporal remote sensing images. The proposed method is able to produce the change detection result on the difference image without a priori assumptions .Firstly, the difference image which is acquired from multitemporal images. Mean shift algorithm is used to reduce noise of difference image and fake change. Then stationary wavelet transform (SWT) is used to extract feature vector of each pixel .The final change detection map is achieved by k-means clustering combined with region growing. The comparisons with the state-of-the-art change detection methods are provided. ©2011 IEEE.

关键词： Change detection

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Reference point-based evolutionary multi-objective optimization for reversible logic circuit synthesis 7

Reference point-based evolutionary multi-objective optimizat...

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2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014

作者： Wang, Xiaoxiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an China School of Computer Science Xi'An Shiyou University Xi'an China

ISBN: (纸本)9781479958382

In this paper, Reversible logic circuit synthesis is formulated as a quantum cost-minimization problem with equality constraint. A new reference-point based evolutionary multi-objective method R-EMO-RLC is specially designed to attack the equality constraint. First, the reference point is determined dynamically according the distribution of solutions. Then, a new crowding comparative operator is fabricated to adapt the uncertainty of constraint violation and objective value aroused by variable length encoding. Experimental results show that R-EMO-RLC can increase the feasible ratio and obtain savings in quantum cost for some benchmarks from recent publications comparing with previously known circuits. © 2014 IEEE.

关键词： Multiobjective optimization

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Multi-objective Gene Expression Programming Based Automatic Clustering Method 9th

Multi-objective Gene Expression Programming Based Automatic ...

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9th International Symposium on Intelligence Computation and Applications, ISICA 2017

作者： Liu, Ruochen Li, Jianxia He, Manman Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an710071 China School of Computer Xi’an Shiyou University Xi’an710065 China

ISBN: (纸本)9789811316470

We have proposed multi-objective Gene Expression Programming (GEP) based automatic clustering method (do not need prior knowledge), which is denoted as MOGEPC. In our algorithm, we adopt GEP based multi-objective optimization, which has a powerful global search ability to optimize the two objective functions, namely, compactness and connectedness simultaneously. We use center-based encoding to generate chromosomal in the encoding phase and expression tree (ET) to decode chromosome to cluster centers. The introduction of multi-objective in GEP is helpful to make the clustering quality of data sets with different structures better. Finally, we apply MOGEPC on artificial data sets, real data sets from UCI. Experiments show that MOGEPC is robust in clustering various data sets without any apriori information. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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New method for improving the performance of SAR image segmentation

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2011年第7期33卷 1700-1705页

作者： Yan, Xue-Ying Jiao, Li-Cheng Wang, Ling-Xia Wan, Hong-Lin Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Considering the shortage of edge preservation and low direction-resolution for SAR image segmentation based on the conventional wavelet transform domain, a new segmentation method is proposed based on Gray-Level Cooccurrence Probability (GLCP) features in the overcomplete Brushlet domain. This method compresses the redundant GLCP features extracted by the adaptive window Gabor filtering in different direction coefficient blocks using compressed sensing, then the Fuzzy C-Mean (FCM) clustering method is utilized to complete the clustering and obtain the segmentation result. The experiment results show that the new method has advantages in the edge preservation and direction extraction, and obtains better segmentation results with respect to other methods.

关键词： Gabor filters

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Automated volumetric assessment of hepatocellular carcinomas using multi-phase-fused dual-attention network

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Biomedical Signal Processing and Control 2025年 109卷

作者： Tong, Nuo Gou, Shuiping Xu, Shanshan Zhou, Yanyan Cao, Yunbao Yi, Jing Chen, Shixin Zhang, Hu Liu, Junde Yu, Jing Luo, Zhonghua Key Lab of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Shaanxi Xi'an710071 China AI-based Big Medical Imaging Data Frontier Research Center Academy of Advanced Interdisciplinary Research Xidian University Shaanxi Xi'an710071 China Guangzhou Institute of Technology Xidian University Guangdong Guangzhou510555 China Department of Interventional Radiology Tangdu Hospital Air Force Medical University of PLA Shaanxi Xi'an710038 China Department of Transfusion Medicine Xijing Hospital Air Force Medical University of PLA Shaanxi Xi'an710032 China Department of Radiology 3201 Hospital Shaanxi Hanzhong723099 China Department of Radiology Xijing Hospital Air Force Medical University of PLA Shaanxi Xi'an710032 China

Objectives: Assessing patients’ responses to Transarterial Chemoembolization (TACE) treatment by analyzing the volume changes of Hepatocellular Carcinomas (HCC) is critical for treatment planning and prognosis. To obtain the precise cancer volumes in real time and assess its responses efficiently, an automated segmentation network is proposed for HCCs and extensively evaluated in this study. Methods: To tackle the low image contrast in the single phase CT images, a network based on the fusion of the multi-phase images and dual-attention mechanism (MFDA-Net) is developed. Despite the U-Net-based backbone network, the Multi-Phase Feature Fusion (MPFF) and Dual-Attention Mechanism (DAM) are proposed to fuse the semantic features from different phases and enhance the spatial and channel-wise discriminative features, respectively. To automate the assessment of the HCC patients’ responses, the correlations between the cancer volumes produced by the segmentation results and the manual delineations were deeply analyzed using diverse metrics. Results: Six metrics were employed for the quantitative evaluation of the proposed MFDA-Net on a multi-phase abdominal CT dataset. Furthermore, Response Evaluation Criteria in Solid Tumors (RECIST) and volumetric RECIST (vRECIST) were utilized to analyze the cancer volumes. Experimental results demonstrated that the proposed MFDA-Net outperforms the other twelve comparison algorithms significantly. Meanwhile, the segmentation results have strong statistical correlations with the manual delineations on both of the RECIST and vRECIST. Conclusions: Extensive experiments validated that the proposed MFDA-Net is an optional method compared with the labor-intensive manual delineations to assess the HCC patients’ responses after the treatments. The source code is available at https://***/mqy-git111/MFDA-Net. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Research of multi-objective optimization algorithms' application in multi-class classification

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2012年第11期40卷 2264-2269页

作者： Shang, Rong-Hua Hu, Chao-Xu Jiao, Li-Cheng Bai, Jing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xidian University Xi'an Shaanxi 710071 China

When Multi-objective Particle Swarm Optimization (MOPSO) optimizes the multi-objective problems of the multiobjective simultaneous learning framework (MSCC), there are only a few nondominated solutions in MOPSO population. In this case, NSGA-II can keep a lot of good dominated solutions in the population, which will help the population optimize, so this paper brought in NSGA-II as the optimization algorithm. The results of experiments show that, under the optimization of NSGA-II, MSCC framework can get better multi-class classifiers. However, dominated solutions can get better classifiers than nondominated solutions. By observing the changing curves of the maximum classification accuracy rate following with the optimization of populations, this paper found that, when dealing with most of the data sets, the maximum accuracy is not improved following the optimization of populations.

关键词： Multiobjective optimization

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Texture classification based on mapping complex directionlet

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Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology 2009年第6期31卷 1332-1336页

作者： Bai, Jing Jia, Jian-Hua Jiao, Li-Cheng Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xi'an 710071 China

Directionlet transform can capture the image singularity due to possessing the multi-direction anisotropic basis functions. A texture classification algorithm based on the Mapping complex Directionlet Transform (M-DT) is proposed, which provides better directionality and approximate shift invariance. By space mapping for the texture image, then complex Directionlet transform is applied to the mapped image, and the multiscale subband coefficient energy feature is used for texture classification. The experiments using texture images from Brodatz and real SAR images indicate the proposed method outperforms wavelets and Multiscale Geometric Analysis (MGA) approaches, the potential application to image analysis by Directionlet is thus proved.

关键词： Synthetic aperture radar

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Pinning consensus in networks with time-varying topology and event-triggered diffusions via impulsive control 35

Pinning consensus in networks with time-varying topology and...

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第35届中国控制会议

作者： HAN Yujuan LU Wenlian LIU Bo CHEN Tianping College of Information Engineering Shanghai Maritime University School of Mathematical Sciences Fudan University Key Laboratory of Intelligent Perception and Image Understanding Xidian University School of Computer/Mathematics Sciences Fudan University

ISBN: (纸本)9781509009107

This paper studies pinning consensus of networks with time-varying topology via impulsive control. Event-triggered rules are employed in diffusion coupling terms to reduce the updating load of the coupled system. Sufficient conditions to pin the network to a prescribed value are proposed. It is proved that in case the coupling matrix across time intervals with length less than some given constant has spanning trees, then the proposed algorithms can realize consensus on the prescribed value when the impulsive controller is imposed randomly on one agent with appropriate impulsive strength and impulse intervals. The imposed pulses can be very sparse, with the impulsive intervals being lower bounded. Two kinds of event-triggered schemes coexist in this pinning control algorithm, one for updating the coupling diffusions;one for applying the impulsive control. Examples with numerical simulation are provided to show the effectiveness of the theoretical results.

关键词： Event-triggered Pinning Impulsive Scheme Time-varying Topology

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Parallel particle swarm optimization for community detection in large-scale networks 11th

Parallel particle swarm optimization for community detection...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Wang, Shanfeng Gong, Maoguo Wu, Yue Qin, Xiaolei Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi’anShaanxi710071 China School of Computer Science and Technology Xidian University Xi’anShaanxi710071 China

ISBN: (纸本)9783319687582

Community detection has great applications in many areas. Many algorithms have been proposed to solve this problem, while these algorithms could not detect communities in large-scale networks effectively and efficiently. In this paper, a parallel particle swarm optimization algorithm based on Apache Spark for community detection is put forward. In this algorithm, an effective representation and a specific updating strategy of discrete particle swarm optimization for parallel computing are designed. Modularity density is used as the objective function and GraphX is used to optimize modularity density parallel. To demonstrate the effectiveness of the proposed algorithm, various experiments on small real-world networks with ground-truth communities are carried out. At the same time, we test the performance of the proposed algorithm on large-scale networks. The experimental results indicate that the proposed method is effective and suitable for community detection in large-scale networks. © Springer International Publishing AG 2017.

关键词： Particle swarm optimization (PSO)

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Neuronal population dynamic model: An analytic approach

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Progress in Natural Science:Materials International 2009年第9期19卷 1159-1163页

作者： Wentao Huang a, Licheng Jiao a, Yuelei Xub, Shiping Ma b, Jianhua Jia a a Institute of intelligent Information Processing, key laboratory of intelligent perception and image understanding of Ministry of Education, Xidian University, P.O. Box 224, Xi’an 710071, China b Department of Avionics Engineering, Engineering College of Air Force Engineering University, Xi’an 710038, China [a]Institute of Intelligent Information Processing Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University P.O. Box 224 Xi’an 710071 China [b]Department of Avionics Engineering Engineering College of Air Force Engineering University Xi’an 710038 China

A novel analytic approach is presented to study the population of excitatory and inhibitory spiking neurons in this paper. The evolution in time of the population dynamic equation is determined by a partial differential equation. A new function is proposed to characterize the population of excitatory and inhibitory spiking neurons, which is different from the population density function discussed by most researchers. And a novel evolution equation, which is a nonhomogeneous parabolic type equation, is derived. From this, the stationary solution and the firing rate of the stationary states are given. Last, by the Fourier transform, the time dependent solution is also obtained. This method can be used to analyze the various dynamic behaviors of neuronal populations.

关键词： Analytic approach Neuronal population Dynamic model Fourier transform

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