检索结果-内蒙古大学图书馆

Temporal Segmentation of Association Football from TV Broadcasting

学校读者我要写书评

暂无评论

Temporal Segmentation of Association Football from TV Broadc...

IEEE International Conference on intelligent Engineering systems

作者： Francisco Siles PRIS-LAB: Pattern Recognition and Intelligent Systems Laboratory School of Electrical Engineering Faculty of Engineering Universidad de Costa Rica

ISBN: (纸本)9781479908271

This paper presents the first module of our system ACE for the automated interpretation of association football games. This module is in charge of the temporal segmentation of the input broadcasting TV video into scenes. The temporal segmentation is based on a new devised dissimilarity measure using the Bhattacharyya distance of the hue and local spatial variance histograms, to incorporate chromatic and structural properties of the images. Great results of extended experiments conducted on the last FIFA's World Cup are presented.

关键词： television broadcasting Footballs segmentation Segmentation color tones Structural properties

Fast and efficent multimodal eye biometrics using projective dictionary pair learning

学校读者我要写书评

暂无评论

Fast and efficent multimodal eye biometrics using projective...

2016 IEEE Congress on Evolutionary Computation, CEC 2016

作者： Das, Abhijit Mondal, Prabir Pal, Umapada Ferrer, Miguel Angel Blumenstein, Michael Institute for Integrated and Intelligent Systems Griffith University QLD Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India IDeTIC University of Las Palmas de Gran Canaria Las Palmas Spain School of Software University of Technology Sydney Australia

ISBN: (纸本)9781509006229

This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l0 or l1-norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments. © 2016 IEEE.

关键词： Biometrics

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

学校读者我要写书评

暂无评论

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

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

Fical: Focal inter-class angular loss for image classification 34

学校读者我要写书评

暂无评论

Fical: Focal inter-class angular loss for image classificati...

34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019

作者： Wei, Xinran Chang, Dongliang Xie, Jiyang Zheng, Yixiao Gong, Chen Zhang, Chuang Ma, Zhanyu Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab Beijing China Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education PCA Lab China Jiangsu Key Lab of Image and Video Understanding for Social Security China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728137230

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets. © 2019 IEEE.

关键词： Image classification

A Cellular Ant Colony Algorithm for Path Planning Using Bayesian Posterior Probability

学校读者我要写书评

暂无评论

A Cellular Ant Colony Algorithm for Path Planning Using Baye...

2019 2nd International Conference on Informatics, Control and Automation (ICA 2019)

作者： Xiu-fen WANG Sheng-yi YANG School of data science and Information Engineering Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University

In order to solve the problem of slow convergence rate in traditional ant colony algorithm for UAV path planning,a new cellular ant colony algorithm is ***,we construct a sector prediction area in grid environment *** we build heuristic and obstacle repulsion functions of target nodes in the prediction *** these functions,we can get the Bayesian conditional probability and posterior estimation of target *** the end,we select the node with the largest posterior estimation as the next path *** simulation results show that the new algorithm has better global search ***,using the sector prediction area makes the planned path more consistent with the UAV flight *** the designed functions in the sector prediction area speeds up the path search process.

关键词： Bayesian ant colony algorithm Sector prediction region Bayesian posteriori probability

Neural network structure design based on N-gauss activation function1

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Lu, Xiangri Ma, Hongbin Zhang, Jingcheng State Key Laboratory of Intelligent Decision and Control for Complex Systems Beijing Institute ofTechnology Beijing China Institute of Pattern Recognition and Intelligent Systems School of Automation Beijing Institute ofTechnology China Institute of Navigation Guidance and Control School of Automation Beijing Institute of

Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to the scale of the data set, and the data set can be trained more deeply, more accurately and more effectively. In this article, we have accepted the experimental results and introduced the core block N − Gauss, N − Gauss, and Swish (Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10, and CIFAR100 respectively. Experiments show that N − Gauss gives full play to the main role of nonlinear modeling of activation functions, so that deep convolutional neural networks have hierarchical nonlinear mapping learning capabilities. At the same time, the training ability of N − Gauss on simple one-dimensional channel small data sets is equivalent to the performance of ReLU and Swish. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

Learning of human-like algebraic reasoning using deep feedforward neural networks

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Cai, Cheng-Hao Ke, Dengfeng Xu, Yanyan Su, Kaile National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Information Science and Technology Beijing Forestry University Institute for Integrated and Intelligent Systems Griffith University

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

Searching for Network Width with Bilaterally Coupled Network

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Su, Xiu You, Shan Xie, Jiyang Wang, Fei Qian, Chen Zhang, Changshui Xu, Chang School of Computer Science Faculty of Engineering The University of Sydney Australia Pattern Recognition and Intelligent Systems Lab. Beijing University of Posts and Telecommunications China SenseTime Research Centre China

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually leveraged to efficiently evaluate the performance w.r.t. different network widths. However, current methods mainly follow a unilaterally augmented (UA) principle for the evaluation of each width, which induces the training unfairness of channels in supernet. In this paper, we introduce a new supernet called Bilaterally Coupled Network (BCNet) to address this issue. In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately. Besides, we propose to reduce the redundant search space and present the BCNetV2 as the enhanced supernet to ensure rigorous training fairness over channels. Furthermore, we leverage a stochastic complementary strategy for training the BCNet, and propose a prior initial population sampling method to boost the performance of the evolutionary search. We also propose the first open-source width benchmark on macro structures named Channel-Bench-Macro for the better comparison of width search algorithms. Extensive experiments on benchmark CIFAR-10 and ImageNet datasets indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Moreover, our method turns out to further boost the performance of NAS models by refining their network widths. For example, with the same FLOPs budget, our obtained EfficientNet-B0 achieves 77.53% Top-1 accuracy on ImageNet dataset, surpassing the performance of original setting by 0.65%. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

Ds-ui: dual-supervised mixture of gaussian mixture models for uncertainty inference

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Xie, Jiyang Ma, Zhanyu Xue, Jing-Hao Zhang, Guoqiang Guo, Jun Pattern Recognition and Intelligent Systems Lab Beijing University of Posts and Telecommunications China Department of Statistical Science University College London United Kingdom School of Electrical and Data Engineering University of Technology Sydney Australia

This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based uncertainty inference (UI) in deep neural network (DNN)-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs’ distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probability density of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. © 2020, CC-BY.

关键词： Gaussian distribution