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International Journal of Digital Content Technology and its Applications 2012年第19期6卷 511-519页

作者： Wang, Ran Wang, Guoyou Liu, Jianguo Chen, Zhong Shi, Yu Image Processing and Intelligent Key Laboratory of Ministry of Education of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 730074 China

The license plate location technique is an important image processing step in license plate recognition system. Vehicle license plates are distinguished from backgrounds using features proposed in existing literatures. However, the effect of location is quite affected by feature selection. In this paper, we propose a method of precise license plate location fusing salient features. The method is mainly divided into three steps. First, candidate license plate regions are detected using improved Harris corner feature with much less time than traditional method. Then, candidates are sifted to only retain license plates based on two salient features named color combination and mean difference which are first proposed in this paper. Finally, the license plates are located precisely according to the projection feature. In experiment, the proposed algorithm was tested with 1942 real images captured in different environment and the license plates are successfully located as 97.6% in average with only 109ms. The experiment results demonstrates the effectiveness and efficient of our algorithm.

关键词： image processing

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Empirically validating software metrics for risk prediction based on intelligent methods

Journal of Digital Information Management

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Journal of Digital Information Management 2007年第3期5卷 99-106页

作者： Xu, Zhihong Zheng, Xin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science Beijing Institute of Technology Beijing 100081 China

The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.

关键词： Bayesian networks

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Mallat fusion for multi-source remote sensing classification

Mallat fusion for multi-source remote sensing classification

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ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications

作者： Dongdong, Cao Qian, Yin Ping, Guo IEEE Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)0769525288

The fusion of multi-source remote sensing data is to offer improved accuracies in land cover classification. The conventional fusion methods such as HIS and PCA can not enhance information and simultaneously preserve high fidelity. Thus, the fused image is not preferable for classification. In this paper, the multi-source remote sensing data fusion based on Mallat algorithm for classification is proposed. The purpose of fusion is to create a new image that is more suitable for recognition. The topic focuses on the pyramid decomposition and choosing coefficients in the fusion process. The performance of proposed method is assessed by statistical methods and its effectiveness also testified by classification accuracies. © 2006 IEEE.

关键词： Remote sensing

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PupilFace: A Cascaded Face Detection and Location Network Fusing Attention 18th

PupilFace: A Cascaded Face Detection and Location Network Fu...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Li, Xiang Zou, Jiancheng Image Processing and Pattern Recognition Laboratory North China University of Technology Beijing100000 China

ISBN: (纸本)9783030893699

Although the development of uncontrolled face detection and location technology have made great progress, there are some problems needing to be solved in more complicated situation, such as massive occlusion and pose variation. In this paper, we propose a robust one-stage face detection and location network named PupilFace. It can locate faces of different sizes at the pixel level in complex scenarios. Specifically, we have made contributions in the following three aspects: (1) Using a lightweight backbone, we can not only detect images of dense faces, but also mark facial landmarks in pictures of various scale. In this paper, the pictures are difficult to detect because of massive occlusion or tiny faces. On the WIDER FACE hard test set, PupilFace performs better than other state-of-the-art networks. (2) The addition of the attention module–Hard Efficient Channel Attention (HECA), proposed by us, enhances the connection between the feature channels and improves the detection performance without reducing the dimension. The parameters and computations of HECA, against the parameters and computations of MobileNetV2 are 9 vs. 3.34M and 5.1e−4 GFLOPs vs. 0.32 GFLOPs. (3) We can employ varying-depths backbones accordingly to different detection and location tasks, so the model can be popularized in different fields. Extra annotations and code have been made available at: https://***/Ideal-maths/PupilFace. © 2021, Springer Nature Switzerland AG.

关键词： Face recognition

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Cost reference particle filter based on adaptive particle swarm optimization in observation uncertainty

Cost reference particle filter based on adaptive particle sw...

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作者： Hu, Zhen-Tao Liu, Xian-Xing Jin, Yong Laboratory of Image Processing and Pattern Recognition Henan University Kaifeng 475001 China

ISBN: (纸本)9789881725592

Aiming at the effective approximation of sampling particle set relative to system state in observation uncertainty, a novel cost reference particle filter based on adaptive particle swarm optimization is proposed. In the new algorithm, the cost function and the risk function are firstly introduced to realize reasonable utilization of the latest observation. In addition, according to the prior modeling information, a new adaptive method is given to solve the selection of limit velocity. And then the movement of particle set towards the region of high weight particle is completed by particle swarm optimization strategy. The algorithm realizes the dynamic combination of the cost reference particle filter and the adaptive particle swarm optimization, and the reliability and stabilize of sampling particle set relative to system state are improved. The theoretical analysis and experimental results show the efficiency of the proposed algorithm. © 2011 Chinese Assoc of Automati.

关键词： Costs

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Research on a novel restoration algorithm of turbulence-degraded images with alternant iterations

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Journal of Systems Engineering and Electronics 2006年第3期17卷 477-482页

作者： Liu Chunsheng Hong Hanyu Zhang Tianxu Inst. for Pattern Recognition and AI State Key Laboratory of Image Processing and Intelligent Control Huazhong Univ. of Science and Technology Wuhan 430074 P. R. China

A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative relations for estimating the turbulent point spread function PSF and object image alternately are derived. The restoration experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estimations of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being convincing.

关键词： turbulence-degraded image image restoration double loops alternant iterations.

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Maintaining imbalance highly dependent medical data using dirichlet process data generation

Maintaining imbalance highly dependent medical data using di...

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2011 6th International Conference on Digital Information Management, ICDIM 2011

作者： Antaresti, Tieta Fanany, Mohamad Ivan Arymurthy, Aniati Murni Pattern Recognition Image Processing and Content-Based Image Retrieval Laboratory Faculty of ComputerScience Universitas Indonesia Depok 16424 Indonesia

ISBN: (纸本)9781457715389

The existence of imbalanced data between one class and another class is an important issue to be considered in a classification problem. One of the well-known data balancing technique is the artificial oversampling, which increase the size of datasets. In this research, multinomial classification was applied to classify some recorded features obtained from a single ECG (electrocardiograph) sensor. Therefore, a Dirichlet process, a dirichlet distribution of cumulative distribution function of each data partition, was needed to model the distribution of the new generated data by also considering the statistical properties of the previous data. Data balancing process had given the result of 77.21% classification accuracy (CA), and 90.9% area under ROC curve (AUC). © 2011 IEEE.

关键词： Distribution functions

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Applications of artificial intelligence to lipid nanoparticle delivery

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Particuology 2024年第7期90卷 88-97页

作者： Ye Yuan Yuqi Wu Jiabei Cheng Kaiyuan Yang Yilin Xia Hongguang Wu Xiaoyong Pan Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaChina Songjiang Research Institute Songjiang District Central HospitalShanghai Jiao Tong University School of MedicineShanghaiChina

Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.

关键词： Artificial intelligence Lipid nanoparticles Antibody Targeted conjugation

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GFENet: Enabling Attention and Adaptive Feature Fusion in Real-time Object Detection 2

GFENet: Enabling Attention and Adaptive Feature Fusion in Re...

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2nd International Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2022

作者： Pan, Chaolin Zhu, Xiaoyang Chu, Jun Nanchang Hangkong University Department of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang China

ISBN: (数字)9781665468749

ISBN: (纸本)9781665468749

Real-time object detection has been a critical component of onboard instrumentation for next-generation autonomous driving. To enable safety and reliability in autonomous driving systems, we must continue advancing its real-time object detection accuracy for future autonomous driving systems in complex environments. Existing works in real-time object detection cannot accurately detect objects with similar appearances and in complex environments. In particular, existing approaches missed many essential features because of the loss of multi-scale contextual features. Therefore, they cannot differentiate objects that have a similar appearance, a low-resolution, and a small size. To solve these issues, and to advance real-time object detection accuracy, we develop GFENet (Global Feature Enhancement Network), an end-to-end real-time object detection framework. First, we introduce a convolutional attention module that integrates spatial and channel-wise features and eliminates ineffective features to enable a strong feature representation of the network, followed by a lightweight residual module that further advances the feature representation by highlighting salient changes in features. Second, we build an adaptive feature fusion module to adaptively learn weights of multi-scale contextual features across layers, aiming to strengthen weights for small-size objects. We comprehensively evaluate GFENet on two leading object detection datasets-PASCAL VOC and MS COCO. The evaluation results (mAP of 82.9 and FPS of 31 on PASCAL VOC;mAP of 39.0 and FPS of 23 on MS COCO) show the outperformance of GFENet compared to competing baseline methods. © 2022 IEEE.

关键词： Instruments Object detection Detectors Feature extraction Real-time systems Safety Reliability

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An image compression-encryption algorithm based on 2-D compressive sensing

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Journal of Computational Information Systems 2013年第24期9卷 10057-10064页

作者： Mo, Yan Zhang, Aidi Zheng, Fen Zhou, Nanrun Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang 330063 China Department of Electronic Information Engineering Nanchang University Nanchang 330031 China

An image compression-encryption algorithm based on 2-D compressive sensing is proposed, which can accomplish encryption and compression simultaneously. The measurements are performed in two directi-ons and the measurement matrices are constructed as partial Hadamard matrices, which are controlled by chaos map. The proposed algorithm is sensitive to the keys and it can resist various attacks. Simulation results verify the validity and reliability of the proposed algorithm. Copyright © 2013 Binary Information Press.

关键词： Compressed sensing

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