检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

6,421 篇 会议
25 篇 期刊文献
3 册 图书

馆藏范围

6,448 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,849 篇 工学
- 3,647 篇 计算机科学与技术...
- 1,431 篇 软件工程
- 790 篇 光学工程
- 302 篇 信息与通信工程
- 242 篇 控制科学与工程
- 219 篇 电气工程
- 201 篇 机械工程
- 80 篇 生物医学工程（可授...
- 68 篇 生物工程
- 67 篇 电子科学与技术（可...
- 64 篇 仪器科学与技术
- 36 篇 建筑学
- 33 篇 力学（可授工学、理...
- 33 篇 土木工程
- 33 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 22 篇 交通运输工程
- 20 篇 材料科学与工程（可...
- 18 篇 化学工程与技术
1,453 篇 理学
- 945 篇 物理学
- 890 篇 数学
- 352 篇 统计学（可授理学、...
- 134 篇 生物学
- 38 篇 系统科学
- 23 篇 化学
160 篇 管理学
- 110 篇 图书情报与档案管...
- 52 篇 管理科学与工程(可...
- 25 篇 工商管理
112 篇 医学
- 112 篇 临床医学
17 篇 法学
- 17 篇 社会学
12 篇 农学
8 篇 教育学
7 篇 艺术学
6 篇 经济学
2 篇 军事学

主题

2,288 篇 computer vision
789 篇 pattern recognit...
637 篇 cameras
629 篇 computer science
568 篇 face recognition
555 篇 layout
510 篇 image segmentati...
509 篇 conferences
498 篇 shape
445 篇 robustness
439 篇 object recogniti...
388 篇 humans
332 篇 feature extracti...
321 篇 training
303 篇 object detection
262 篇 image recognitio...
257 篇 application soft...
246 篇 lighting
238 篇 image reconstruc...
237 篇 computational mo...

机构

41 篇 microsoft resear...
26 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
20 篇 department of co...
19 篇 swiss fed inst t...
19 篇 carnegie mellon ...
18 篇 department of co...
17 篇 department of in...
17 篇 the robotics ins...
17 篇 institute of com...
16 篇 univ sci & techn...
16 篇 robotics institu...
15 篇 tsinghua univ pe...
14 篇 department of el...
14 篇 school of comput...
14 篇 school of comput...
13 篇 univ maryland co...
13 篇 microsoft resear...
13 篇 microsoft resear...

作者

39 篇 timofte radu
28 篇 s.k. nayar
24 篇 huang thomas s.
23 篇 xiaoou tang
22 篇 t. kanade
20 篇 t.s. huang
19 篇 van gool luc
19 篇 t. darrell
19 篇 chellappa rama
18 篇 nayar shree k.
17 篇 a.k. jain
17 篇 a. zisserman
17 篇 jain anil k.
16 篇 g. healey
16 篇 torralba antonio
16 篇 heung-yeung shum
16 篇 zisserman andrew
16 篇 l. van gool
15 篇 m. shah
15 篇 ji qiang

语言

6,447 篇 英文
2 篇 其他

检索条件"任意字段=1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992"

共 6449 条记录，以下是581-590 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Sports Field Registration via Keypoints-aware Label Condition

Sports Field Registration via Keypoints-aware Label Conditio...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Chu, Yen-Jui Su, Jheng-Wei Hsiao, Kai-Wen Lien, Chi-Yu Fan, Shu-Ho Hu, Min-Chun Lee, Ruen-Rone Yao, Chih-Yuan Chu, Hung-Kuo Natl Tsing Hua Univ Hsinchu Taiwan Delta Res Ctr Hsinchu Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose a novel deep learning framework for sports field registration. The typical algorithmic flow for sports field registration involves extracting field-specific features (e.g., corners, lines, etc.) from field image and estimating the homography matrix between a 2D field template and the field image using the extracted features. Unlike previous methods that strive to extract sparse field features from field images with uniform appearance, we tackle the problem differently. First, we use a grid of uniformly distributed keypoints as our field-specific features to increase the likelihood of having sufficient field features under various camera poses. Then we formulate the keypoints detection problem as an instance segmentation with dynamic filter learning. In our model, the convolution filters are generated dynamically, conditioned on the field image and associated keypoint identity, thus improving the robustness of prediction results. To extensively evaluate our method, we introduce a new soccer dataset, called TS-WorldCup, with detailed field markings on 3812 time-sequence images from 43 videos of SoccerWorld Cup 2014 and 2018. The experimental results demonstrate that our method outperforms state-of-the-arts on the TS-WorldCup dataset in both quantitative and qualitative evaluations. Both the code and dataset are available online(1).

关键词： Image segmentation Predictive models Feature extraction Robustness pattern recognition Sparse matrices Task analysis

来源：评论

学校读者我要写书评

暂无评论

MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning

MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incre...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Chi, Zhixiang Gu, Li Liu, Huan Wang, Yang Yu, Yuanhao Tang, Jin Noah's Ark Lab Huawei Technologies McMaster University Canada University of Manitoba Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we tackle the problem of few-shot class incremental learning (FSCIL). FSCIL aims to incrementally learn new classes with only a few samples in each class. Most existing methods only consider the incremental steps at test time. The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i.e. incrementally learning new classes) during testing. Those methods are sub-optimal due to the misalignment between the training objectives and what the methods are expected to do during evaluation. In this work, we proposed a bi-level optimization based on meta-learning to directly optimize the network to learn how to incrementally learn in the setting of FSCIL. Concretely, we propose to sample sequences of incremental tasks from base classes for training to simulate the evaluation protocol. For each task, the model is learned using a meta-objective such that it is capable to perform fast adaptation without forgetting. Furthermore, we propose a bi-directional guided modulation, which is learned to automatically modulate the activations to reduce catastrophic forgetting. Extensive experimental results demonstrate that the proposed method outperforms the baseline and achieves the state-of-the-art results on CIFARIOO, MiniImageNet, and CUB200 datasets. © 2022 ieee.

关键词： Training Adaptation models Protocols Modulation Bidirectional control Power capacitors pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pods

Transfer Learning from Synthetic In-vitro Soybean Pods Datas...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Si Zheng, Lihua Chen, Xieyuanli Zabawa, Laura Zhang, Man Wang, Minjuan China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China China Agr Univ Minist Educ Key Lab Smart Agr Syst Beijing Peoples R China Univ Bonn Inst Geodesy & Geoinformat Bonn Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning-based methods can achieve accurate training and strong generalization capabilities, but it demands massive labeled data, which is often a limitation, especially for agricultural applications. As lacking the labeled data to train an in-situ segmentation model for on-branch soybean pods, we propose a transfer learning from synthetic in-vitro soybean pods. First, we present a novel automated image generation method to rapidly generate a synthetic in-vitro soybean pods dataset with plenty of annotated samples. The in-vitro soybean pods samples are overlapped to simulate the frequently physically touching of on-branch soybean pods. Then, we design a two-step transfer learning. In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset). In the second step, transferring from simulation to reality is performed by finetuning on a few real-world mature soybean plant samples. The experimental results show the effectiveness of the proposed two-step transfer learning method, such that AP50 was 0.80 for the real-world mature soybean plant test dataset, which is higher than that of direct adaptation and its AP50 was 0.77. Furthermore, the visualizations of in-situ segmentation results of on-branch soybean pods show that our method performs better than other methods, especially when soybean pods overlap densely.

关键词： Deep learning Transfer learning computer vision Instance segmentation Plant phenotyping

来源：评论

学校读者我要写书评

暂无评论

Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment

Enhancing Multi-Camera People Tracking with Anchor-Guided Cl...

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Huang, Hsiang-Wei Yang, Cheng-Yen Jiang, Zhongyu Kim, Pyong-Kun Lee, Kyoungoh Kim, Kwangju Ramkumar, Samartha Mullapudi, Chaitanya Jang, In-Su Huang, Chung-I Hwang, Jenq-Neng University of Washington Information Processing Lab United States Electronics and Telecommunications Research Institute Korea Republic of National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on cvpr AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://***/ipl-uw/AIC23-Track1-UWIPL-ETRI. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Lost in Compression: the Impact of Lossy Image Compression on Variable Size Object Detection within Infrared Imagery

Lost in Compression: the Impact of Lossy Image Compression o...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bhowmik, Neelanjan Barker, Jack W. Gaus, Yona Falinie A. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Lossy image compression strategies allow for more efficient storage and transmission of data by encoding data to a reduced form. This is essential enable training with larger datasets on less storage-equipped environments. However, such compression can cause severe decline in performance of deep Convolution Neural Network (CNN) architectures even when mild compression is applied and the resulting compressed imagery is visually identical. In this work, we apply the lossy JPEG compression method with six discrete levels of increasing compression {95, 75, 50, 15, 10, 5} to infrared band (thermal) imagery. Our study quantitatively evaluates the affect that increasing levels of lossy compression has upon the performance of characteristically diverse object detection architectures (Cascade-RCNN, FSAF and Deformable DETR) with respect to varying sizes of objects present in the dataset. When training and evaluating on uncompressed data as a baseline, we achieve maximal mean Average Precision (mAP) of 0.823 with Cascade R-CNN across the FLIR dataset, outperforming prior work. The impact of the lossy compression is more extreme at higher compression levels (15, 10, 5) across all three CNN architectures. However, re-training models on lossy compressed imagery notably ameliorated performances for all three CNN models with an average increment of similar to 76% (at higher compression level 5). Additionally, we demonstrate the relative sensitivity of differing object areas {tiny, small, medium, large} with respect to the compression level. We show that tiny and small objects are more sensitive to compression than medium and large objects. Overall, Cascade R-CNN attains the maximal mAP across most of the object area categories.

关键词： Training Image coding Sensitivity Transform coding Object detection computer architecture Transformers

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Timofte, Radu Gong, Yuan Lao, Shanshan Shi, Shuwei Wang, Jiahao Yang, Sidi Wu, Tianhe Xia, Weihao Yang, Yujiu Cao, Mingdeng Heng, Cong Fu, Lingzhi Zhang, Rongyu Zhang, Yusheng Wang, Hao Song, Hongjian Wang, Jing Fan, Haotian Hou, Xiaoxia Sun, Ming Li, Mading Zhao, Kai Yuan, Kun Kong, Zishang Wu, Mingda Zheng, Chuanchuan Conde, Marcos, V Burchi, Maxime Feng, Longtao Zhang, Tao Li, Yang Xu, Jingwen Wang, Haiqiang Liao, Yiting Li, Junlin Xu, Kele Sun, Tao Xiong, Yunsheng Keshari, Abhisek Komal, Komal Thakur, Sadbhawana Jakhetiya, Vinit Subudhi, Badri N. Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China SenseTime Res Beijing Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China UCL London England Netease Hangzhou Peoples R China Kuaishou Beijing Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany ByteDance Beijing Peoples R China Key Lab Parallel & Distributed Proc Beijing Peoples R China Indian Inst Technol Jammu Jagti Jammu & Kashmir India Indian Inst Technol Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata W Bengal India Indian Inst Technol Jodhpur Jodhpur Rajasthan India Natl Taiwan Univ New Taipei Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reports on the NTIRE 2022 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at cvpr 2022. This challenge is held to address the emerging challenge of IQA by perceptual image processing algorithms. The output images of these algorithms have completely different characteristics from traditional distortions and are included in the PIPAL dataset used in this challenge. This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods. The challenge has 192 and 179 registered participants for two tracks. In the final testing stage, 7 and 8 participating teams submitted their models and fact sheets. Almost all of them have achieved better results than existing IQA methods, and the winning method can demonstrate state-of-the-art performance.

关键词： Image quality computer vision conferences Computational modeling Market research Distortion pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Exploring Motion Information for Distractor Suppression in Visual Tracking

Exploring Motion Information for Distractor Suppression in V...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Kaiwen Gao, Jin Liu, Haowei Li, Liang Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch AI Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China Beijing Inst Basic Med Sci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the past few years, Siamese networks have achieved outstanding improvements in visual object tracking. However, visual distractors with similar semantics can be easily misclassified as the target by Siamese networks and may consequently result in the drift problem. Besides, the Hanning window penalty, which is generally used to suppress distractors, could fail in many challengeable scenes. Notably, most failures violate the assumption of motion continuity. Thus, in this work, we explore motion information to mitigate the drift problem in visual tracking. First, we introduce a simple linear Kalman filter to predict the bounding box of the target in the current frame, which acts as a reference for decisions. Second, an IoU-Guided penalty is assembled in the post-processing to suppress distractors effectively. It's worth mentioning that our method is almost cost-free. We conduct numerous experimental validations and analyses of our approach on several challenging sequences and datasets. Our tracker runs at approximately 40 fps and performs well on those sequences which include the Background Clutter attribute. Finally, by simultaneously integrating the IoU-Guidedpenalty and the Hanning window penalty with a strong baseline tracker TransT [7], our method achieves favorable gains by 69.1 -> 71.5, 65.7 -> 66.7, 64.9 -> 65.9 success on OTB-100 [32], LaSOT [12], NFS[18].

关键词： Training Visualization computer vision Target tracking Statistical analysis conferences Semantics

来源：评论

学校读者我要写书评

暂无评论

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Unsupervised domain adaptation and super resolution on drone...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Albert, Paul Saadeldin, Mohamed Narayanan, Badri Mac Namee, Brian Hennessy, Deirdre O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Sch Elect Engn Dublin Ireland Univ Coll Dublin Sch Comp Sci Dublin Ireland Insight Ctr Data Analyt Dublin Ireland TEAGASC Dublin Ireland VistaMilk Dublin Ireland

ISBN: (纸本)9781665487399

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together [GRAPHICS] with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way f

关键词： Deep learning computer vision Image resolution Costs Estimation Prediction algorithms Cameras

来源：评论

学校读者我要写书评

暂无评论

Contactless Blood Pressure Measurement via Remote Photoplethysmography with Synthetic Data Generation Using Generative Adversarial Network

Contactless Blood Pressure Measurement via Remote Photopleth...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wu, Bing-Fei Chiu, Li-Wen Wu, Yi-Chiao Lai, Chun-Chih Chu, Pao-Hsien Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn New Taipei Taiwan Chang Gung Univ Chang Gung Mem Hosp Taoyuan Taiwan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deriving blood pressure in a non-invasive way via photoplethysmography (PPG) signals has become a familiar topic. With the knowledge of the relation between PPG and blood pressure, we expect to further make the measurement contactless for convenience reasons. An alternative signal source is remote photoplethysmography (rPPG) signals. There are mainly two kinds of approaches for exploiting blood pressure through PPG signals, one is by calculating the pulse transit time of the arterial pulse wave at two consecutive sites and the other is based on waveform feature analysis from a single signal. The calibration procedure is necessary for the former way, which leads to some limitations in general use. On the other hand, the properties of the rPPG waveform are far from PPG signals. Hence, the known waveform features in PPG signals are hard to be leveraged in the case of rPPG signals. Recently, convolutional neural networks are also applied for solving this problem. However, the lack of data is an obstacle to the training procedure and evaluation. In this study, a multichannel rPPG-based blood pressure estimator is proposed. To ease the data scarcity issue, the generative adversarial network is adopted to augment synthetic waveform data. Besides, as we know that some physiological states like age and BMI are dominant factors in blood pressure. InfoGAN is chosen in this work to generate the synthetic data with the blood pressure value fluctuating correspondingly to the controlled age and BMI combination. The proposed model outperforms the state-of-the-art methods on MIMIC III and Cuffless datasets. With the synthetic data generation, the mean absolute error (MAE) is reduced to 6.72 and 5.95 mmHg in MAP and DBP respectively. The standard deviations of the MAEs are also reduced. In the rPPG case, the MAE of SBP is 9.13 and 8.76 mmHg for DBP.

关键词： Training Interference Photoplethysmography Generative adversarial networks Blood pressure Pressure measurement pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Time Lens++: Event-based Frame Interpolation with Parametric Nonlinear Flow and Multi-scale Fusion

Time Lens++: Event-based Frame Interpolation with Parametric...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Tulyakov, Stepan Bochicchio, Alfredo Gehrig, Daniel Georgoulis, Stamatios Li, Yuanyou Scaramuzza, Davide Huawei Technologies Zurich Research Center Switzerland Univ. of Zurich Eth Zurich Dept. of Informatics Dept. of Neuroinformatics Czech Republic

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still suffer from (i) brittle image-level fusion of complementary interpolation results, that fails in the presence of artifacts in the fused image, (ii) potentially temporally inconsistent and inefficient motion estimation procedures, that run for every inserted frame and (iii) low contrast regions that do not trigger events, and thus cause events-only motion estimation to generate artifacts. Moreover, previous methods were only tested on datasets consisting of planar and far-away scenes, which do not capture the full complexity of the real world. In this work, we address the above problems by introducing multi-scale feature-level fusion and computing one-shot non-linear inter-frame motion-which can be efficiently sampled for image warping-from events and images. We also collect the first large-scale events and frames dataset consisting of more than 100 challenging scenes with depth variations, captured with a new experimental setup based on a beamsplitter. We show that our method improves the reconstruction quality by up to 0.2 dB in terms of PSNR and up to 15% in LPIPS score. © 2022 ieee.

关键词： Interpolation computer vision Motion estimation Memory management Dynamics Cameras pattern recognition

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 55 56 57 58 59 60 61 62 63 64 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：