检索结果-内蒙古大学图书馆

5th Asian conference on pattern recognition, ACPR 2019

作者： Wan, Yiming Gao, Wei Wu, Yihong NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030414030

this paper proposes a novel deep learning based approach for monocular visual odometry (VO) called FlowVO-Net. Our approach utilizes CNN to extract motion information between two consecutive frames and employs Bi-directional convolution LSTM (Bi-ConvLSTM) for temporal modelling. ConvLSTM can encode not only temporal information but also spatial correlation, and the bidirectional architecture enables it to learn the geometric relationship from image sequences pre and post. Besides, our approach jointly predicts optical flow as an auxiliary task in a self-supervised way by measuring photometric consistency. Experiment results indicate competitive performance of the proposed FlowVO-Net to the state-of-art methods. © Springer Nature Switzerland AG 2020.

关键词： Optical flows

来源：评论

学校读者我要写书评

暂无评论

Real-Time Face Occlusion recognition Algorithm Based on Feature Fusion 14th

Real-Time Face Occlusion Recognition Algorithm Based on Feat...

引用

14th chinese conference on Biometric recognition (CCBR)

作者： Zhang, Xiangde Zheng, Bin Li, Yuanjie Yang, Lianping Northeastern Univ Coll Sci Shenyang Liaoning Peoples R China

ISBN: (纸本)9783030314569;9783030314552

the real-time face occlusion recognition is an important computer vision problem, especially for the public safety field. In order to construct a real-time face occlusion recognition system, this paper first established a large occlusion face database. then, this paper proposed a face occlusion recognition algorithm based on the fusion of histogram of oriented gradient(HOG) and local binary pattern(LBP), the experimental results show that the occlusion face recall rate and the unobstructed face recall rate are 92.03% and 93.58% respectively, the speed is about 12.26 ms. Finally, taking into account time factor, this paper established a lightweight deep neural network based on AlexNet with an occlusion face recall rate and an unobstructed face recall rate of 91.79% and 91.42% respectively, and the speed is approximately 22.92 ms. the experimental results show that the face occlusion recognition method based on HOG+LBP features not only improves the recognition rate of occlusion face, but also reduces the time complexity, and illustrates the effectiveness of the algorithm.

关键词： Face occlusion recognition Histogram of oriented gradient Local binary pattern Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

An Online Platform for Underwater Image Quality Evaluation 24th

An Online Platform for Underwater Image Quality Evaluation

引用

24th International conference on pattern recognition (ICPR)

作者： Li, Chau Yi Mazzon, Riccardo Cavallaro, Andrea Queen Mary Univ London Ctr Intelligent Sensing London England

ISBN: (纸本)9783030057923;9783030057916

With the miniaturisation of underwater cameras, the volume of available underwater images has been considerably increasing. However, underwater images are degraded by the absorption and scattering of light in water. Image processing methods exist that aim to compensate for these degradations, but there are no standard quality evaluation measures or testing datasets for a systematic empirical comparison. For this reason, we propose PUIQE, an online platform for underwater image quality evaluation, which is inspired by other computer vision areas whose progress has been accelerated by evaluation platforms. PUIQE supports the comparison of methods through standard datasets and objective evaluation measures: quality scores for images uploaded on the platform are automatically computed and published in a leader-board, which enables the ranking of methods. We hope that PUIQE will stimulate and facilitate the development of underwater image processing algorithms to improve underwater images.

关键词： Underwater image processing Evaluation platform Benchmark datasets Underwater image enhancement

来源：评论

学校读者我要写书评

暂无评论

Human and Machine recognition of Transportation Modes from Body-Worn Camera Images 8

Human and Machine Recognition of Transportation Modes from B...

引用

Joint 8th International conference on Informatics, Electronics and vision (ICIEV) / 3rd International conference on Imaging, vision and pattern recognition (icIVPR) / International conference on Activity and Behavior Computing (ABC)

作者： Richoz, Sebastien Ciliberto, Mathias Wang, Lin Birch, Philip Gjoreski, Hristijan Perez-Uribe, Andres Roggen, Daniel Univ Sussex Wearable Technol Lab Brighton E Sussex England Queen Mary Univ London Ctr Intelligent Sensing London England Univ Sussex Engn & Informat Brighton E Sussex England Ss Cyril & Methodius Univ Fac Elect Engn & Informat Technol Skopje North Macedonia Univ Appl Sci Inst Informat & Commun Technol Yverdon Switzerland

ISBN: (纸本)9781728107882

computer vision techniques applied on images opportunistically captured from body-worn cameras or mobile phones offer tremendous potential for vision-based context awareness. In this paper, we evaluate the potential to recognise the modes of locomotion and transportation of mobile users, by analysing single images captured by body-worn cameras. We evaluate this with the publicly available Sussex-Huawei Locomotion and Transportation Dataset, which includes 8 transportation and locomotion modes performed over 7 months by 3 users. We present a baseline performance obtained through crowd sourcing using Amazon Mechanical Turk. Humans infered the correct modes of transportations from images with an F1-score of 52%. the performance obtained by five state-of-the-art Deep Neural Networks (VGG16, VGG19, ResNet50, MobileNet and DenseNet169) on the same task was always above 71.3% F1-score. We characterise the effect of partitioning the training data to fine-tune different number of blocks of the deep networks and provide recommendations for mobile implementations.

关键词： Activity recognition Body-worn camera computer vision Deep learning Crowd sourcing Mechanical Turk

来源：评论

学校读者我要写书评

暂无评论

Ensembled Tricks for Instance Segmentation

Ensembled Tricks for Instance Segmentation

引用

International Wireless Communications and Mobile Computing conference, IWCMC

作者： Runze Zhang Liang Jin Yongfang Chen Zhenhua Guo Kun Zhao Yaqian Zhao State Key Laboratory of High-end Server & Storage Technology Inspur Electronic Information Industry Co. Ltd Jinan China Guangdong Inspur Big Data Research Co. Ltd. Guangzhou China

ISBN: (数字)9781728131290

ISBN: (纸本)9781728131306

computer vision has attracted more and more attention with the fast development of deep learning. the instance segmentation area, which extends the Object detection, can better help us comprehend the surrounding environments. In this paper, we ensembled the tricks that can strengthen the model performance for instance segmentation. We do the ablation experiments for the MS-COCO datasets and LVIS datasets. the results demonstrate that the selected tricks can greatly boost the performance. With our tricks, our model achieves the 7th on the LVIS Challenge Track for ICCV 2019 workshop.

关键词： computer vision conferences Head Task analysis Image segmentation Object detection pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Analysis of Landscape pattern Spatial Scale in Middle and Upper Reaches of Meijiang River Basin 7th

Analysis of Landscape Pattern Spatial Scale in Middle and Up...

引用

7th International conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International conference on Geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Chen, Yuchan Zhang, Zhengdong Yang, Chuanxun Yang, Yang Zhang, Chen Han, Liusheng Yang, Ji Han, Xiangyu Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China School of Geography South China Normal University Guangzhou510631 China Guangzhou511458 China University of Chinese Academy of Sciences Beijing100049 China State Grid Chengdu Electric Supply Company Chengdu610000 China

ISBN: (纸本)9789811561054

the scale research of landscape pattern is an important basis for the study of spatiotemporal evolution of landscape pattern and the scientific and reasonable allocation of landscape pattern. this paper takes the middle and upper reaches of Meijiang river as the research area. Combined with spatial grain size analysis and extent size analysis, the spatial scale effect was studied to select the optimal research scale in this research area. the results show that most of the appropriate grain size of landscape pattern indexes are in range of 90–150 m, and the optimal grain size scale is 150 m in Middle and Upper Reaches of Meijiang River Basin. At the grain size scale of 150 m, the spatial self-correlation of landscape pattern index over 50% is the highest at the extent size of 300 m, and the optimal extent size scale is 300 m in this basin. this paper determines the optimal scale of landscape pattern in the middle and upper reaches of Meijiang river basin, which is of great significance to the ecological balance and sustainable development of the basin. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Rivers

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13536 LNCS卷 v-vi页

作者： Tan, Tieniu Guo, Yike Lai, Jianhuang Zhang, Jianguo Yu, Shiqi Zhang, Zhaoxiang Yuen, Pong C. Han, Junwei Southern University of Science and Technology Shenzhen China Institute of Automation Chinese Academy of Sciences Beijing China Hong Kong Baptist University Hong Kong Northwestern Polytechnical University Xi’an China Sun Yat-sen University Guangzhou China

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Computer Science (including subseries Lectu...

引用

Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2022年 13535 LNCS卷 v-vi页

作者： Yu, Shiqi Zhang, Zhaoxiang Yuen, Pong C. Han, Junwei Tan, Tieniu Guo, Yike Lai, Jianhuang Zhang, Jianguo Southern University of Science and Technology Shenzhen China Institute of Automation Chinese Academy of Sciences Beijing China Hong Kong Baptist University Hong Kong Northwestern Polytechnical University Xi’an China Sun Yat-sen University Guangzhou China

来源：评论

学校读者我要写书评

暂无评论

Large Batch Optimization for Object Detection: Training COCO in 12 minutes 16th

Large Batch Optimization for Object Detection: Training COCO...

引用

16th European conference on computer vision, ECCV 2020

作者： Wang, Tong Zhu, Yousong Zhao, Chaoyang Zeng, Wei Wang, Yaowei Wang, Jinqiao Tang, Ming National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China ObjectEye Inc. Beijing China Peking University Beijing China Peng Cheng Laboratory Shenzhen China NEXWISE Co. Ltd. Guangzhou China

ISBN: (纸本)9783030585884

Most of existing object detectors usually adopt a small training batch size (e.g. 16), which severely hinders the whole community from exploring large-scale datasets due to the extremely long training procedure. In this paper, we propose a versatile large batch optimization framework for object detection, named LargeDet, which successfully scales the batch size to larger than 1K for the first time. Specifically, we present a novel Periodical Moments Decay LAMB (PMD-LAMB) algorithm to effectively reduce the negative effects of the lagging historical gradients. Additionally, the Synchronized Batch Normalization (SyncBN) is utilized to help fast convergence. With LargeDet, we can not only prominently shorten the training period, but also significantly improve the detection accuracy of sparsely annotated large-scale datasets. For instance, we can finish the training of ResNet50 FPN detector on COCO within 12 min. Moreover, we achieve 12.2% mAP@0.5 absolute improvement for ResNet50 FPN on Open Images by training with batch size 640. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Efficient pattern recognition Algorithm Including a Fast Retina Keypoint FPGA Implementation 29

Efficient Pattern Recognition Algorithm Including a Fast Ret...

引用

29th International conference on Field-Programmable Logic and Applications (FPL)

作者： Kalms, Lester Hajduk, Maximilian Goehringer, Diana Tech Univ Dresden Dresden Germany

ISBN: (纸本)9781728148847

the field of computer vision is continuously increasing and becoming more complex and power demanding. Using feature detection and description allows a fast object detection without needing big databases. FPGAs are predestined for different requirements, like real-time and power constraints, which are important in many application areas. this work proposes a new pattern recognition algorithm, based on an improved Accelerated KAZE (AKAZE) detector and Fast Retina Keypoint (FREAK) descriptor. Our software implementation increased the repeatability in comparison to the original algorithm using optimized configurations. the percentage of correct matching features between two images (repeatability) increased from 85.7% to 91.4%, while the computation time decreases from 70.3ms to 24.9ms. Furthermore, we present an efficient FPGA implementation of the FREAK descriptor. the accelerator processes 2048 features at 73.4 frames per second;achieving a repeatability of 90.9%, while being optimized for resource utilization and memory bandwidth consumption. Additionally, we show an efficient Integral Image implementation that processes four image pixels per clock cycle at a high frequency (204 MHz on xc7z020clg484-1) consuming minimum resources.

关键词： computer vision pattern recognition FPGA Repeatability Fast Retina Keypoint Integral Image

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：