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

15,887 篇 会议
363 篇 期刊文献
193 册 图书

馆藏范围

16,443 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,341 篇 工学
- 5,433 篇 计算机科学与技术...
- 2,997 篇 软件工程
- 2,169 篇 电气工程
- 1,867 篇 信息与通信工程
- 1,130 篇 控制科学与工程
- 890 篇 光学工程
- 646 篇 机械工程
- 644 篇 电子科学与技术（可...
- 539 篇 仪器科学与技术
- 539 篇 生物医学工程（可授...
- 477 篇 生物工程
- 226 篇 测绘科学与技术
- 219 篇 交通运输工程
- 205 篇 材料科学与工程（可...
- 199 篇 航空宇航科学与技...
- 163 篇 化学工程与技术
- 160 篇 网络空间安全
- 141 篇 安全科学与工程
4,017 篇 理学
- 2,399 篇 物理学
- 1,363 篇 数学
- 527 篇 生物学
- 368 篇 统计学（可授理学、...
- 341 篇 系统科学
- 207 篇 化学
1,522 篇 医学
- 1,349 篇 临床医学
- 201 篇 特种医学
- 197 篇 基础医学(可授医学...
1,056 篇 管理学
- 614 篇 管理科学与工程(可...
- 482 篇 图书情报与档案管...
- 139 篇 工商管理
84 篇 法学
66 篇 农学
45 篇 经济学
43 篇 教育学
37 篇 军事学
32 篇 文学
26 篇 艺术学
1 篇 哲学

主题

2,783 篇 image processing
1,552 篇 image segmentati...
1,512 篇 signal processin...
867 篇 feature extracti...
658 篇 image edge detec...
635 篇 cameras
633 篇 signal processin...
612 篇 real-time system...
577 篇 image reconstruc...
517 篇 image coding
498 篇 algorithm design...
402 篇 deep learning
399 篇 clustering algor...
398 篇 image color anal...
384 篇 object detection
370 篇 image enhancemen...
362 篇 accuracy
334 篇 hardware
323 篇 image analysis
313 篇 computer vision

机构

40 篇 xi'an jiaotong u...
32 篇 northwestern pol...
31 篇 xi an jiao tong ...
31 篇 school of electr...
29 篇 school of electr...
26 篇 university of ch...
24 篇 xi an jiao tong ...
22 篇 xi an jiao tong ...
22 篇 national laborat...
21 篇 signal and image...
19 篇 samara national ...
18 篇 xidian universit...
16 篇 school of inform...
16 篇 institute of art...
15 篇 tsinghua univers...
15 篇 department of el...
15 篇 school of automa...
15 篇 national key lab...
15 篇 school of comput...
14 篇 school of electr...

作者

38 篇 chen xi
29 篇 xi chen
24 篇 nanning zheng
24 篇 licheng jiao
20 篇 li xi
20 篇 liu ying
18 篇 xuelong li
17 篇 anon
14 篇 xi ning
14 篇 wei li
14 篇 yuan yuan
13 篇 de-shuang huang
13 篇 yanning zhang
13 篇 xi li
12 篇 jie yang
12 篇 yang xi
12 篇 ying liu
11 篇 p. pirsch
11 篇 liu yang
11 篇 mingyi he

语言

16,016 篇 英文
282 篇 其他
135 篇 中文
20 篇 土耳其文
3 篇 葡萄牙文
2 篇 法文
2 篇 俄文
1 篇 西班牙文

检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16443 条记录，以下是801-810 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：

The Impact of Preprocessing on Face Recognition using Pseudorandom Pixel Placement 29

The Impact of Preprocessing on Face Recognition using Pseudo...

引用

29th International conference on systems, Signals and image processing (IWSSIP)

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Nurzaenab Kanazawa Univ Dept Elect Engn & Comp Sci Kanazawa Japan Kumamoto Univ Ctr Management Informat Technol Kumamoto Japan STMIK AKBA Study Program Informat Engn Makassar Indonesia

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

The condition of face images, data processing algorithms, and hardware capabilities can influence the accuracy of face recognition. Several studies have been conducted to increase performance of face recognition. One of the steps is to create or even improve the methods in preprocessing as one of the essential steps that can affect accuracy. This paper proposed a pseudorandom pixel placement method applied to the preprocessing step in face recognition to know the impact on accuracy. Eight face objects were used in this study. One face image for one object as training data was taken via a single-lens digital reflex camera and a smartphone. One video for one object was taken from Closed Circuit Television with two different placement angle conditions for testing data. The experiment was carried out with four variations of the basic resolution size of the face image in the testing data to see the performance of the proposed method. The result is five of eight face objects have improved accuracy than without using pseudorandom pixel placement. The best average accuracy result using the proposed method is 63.76% higher than without using the proposed method with a value of 60.09%, so preprocessing using the pseudorandom pixel placement on face recognition can increase accuracy.

关键词： Face Recognition Pseudorandom Pixel Placement One image Training Data image processing Computer Vision

来源：评论

学校读者我要写书评

暂无评论

Post-processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological images

Post-Processing of Thresholding or Deep Learning Methods for...

引用

18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

DEEP image FUSION ACCOUNTING FOR INTER-image VARIABILITY 56

DEEP IMAGE FUSION ACCOUNTING FOR INTER-IMAGE VARIABILITY

引用

56th Asilomar conference on Signals, systems, and Computers

作者： Wang, xiuheng Borsoi, Ricardo Augusto Richard, Cedric Chen, Jie Univ Cote Azur CNRS OCA Nice France Univ Lorraine CNRS CRAN Nancy France Northwestern Polytech Univ Inst Res & Dev Shenzhen Peoples R China

ISBN: (纸本)9781665459068

Hyperspectral and multispectral image fusion (HMIF) allows us to overcome inherent hardware limitations of hyperspectral imaging systems with respect to their lower spatial resolution. However, existing algorithms fail to consider realistic image acquisition conditions, or to leverage the powerful representation capacity of deep neural networks. This paper introduces a general imaging model which considers inter-image variability of data from heterogeneous sources, and formulates the optimization problem. Then it presents a new image fusion method that, on the one hand, solves the optimization problem accounting for inter-image variability with an iteratively reweighted scheme and, on the other hand, leverages unsupervised light-weight CNN-based denoisers to learn realistic image priors from data. Its performance is illustrated with real data that suffer from inter-image variability.

关键词： Hyperspectral data multispectral data inter-image variability image fusion deep learning

来源：评论

学校读者我要写书评

暂无评论

Advanced HLS for Reconfigurable image processing in Big Data systems 12th

Advanced HLS for Reconfigurable Image Processing in Big Da...

引用

12th International conference on Big Data and Artificial Intelligence, BDA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Noida201309 India

ISBN: (纸本)9783031818202

This paper introduces novel HLS techniques for reconfigurable and memory-efficient image processing within deep learning frameworks, addressing inherent limitations of current deep learning accelerators (DLAs) due to their dependency on the host processor, leading to pipeline delays and reduced throughput. The proposed approach emphasizes parallelization, on-chip buffering, and scalability, enhancing computational efficiency and throughput for AI applications in big data and edge computing environments. Methodologically, the paper emphasizes mathematical analysis, hardware architecture specification, HLS modeling, Verilog RTL implementation. By addressing these challenges, this work contributes to advancing the performance capabilities of DLAs in deep learning inference tasks. The algorithms integrates seamlessly with DLA IP subsystems, such as Axi interfaces, external (DRAM) and internal (SRAM) memory interfaces, and activation/pooling engines. Key contributions include advanced algorithm design to enhance memory efficiency and reconfigurability, essential for adapting to diverse hardware architectures. Experimental validation demonstrates substantial improvements in computational speed and resource utilization compared to traditional methods, making it suitable for intensive AI tasks in big data analytics. In conclusion, this research presents a framework for integrating HLS techniques into hardware accelerators, facilitating efficient image processing across diverse applications in big data and edge computing. These advancements aim to bolster DLAs’ capabilities in handling complex AI workloads effectively, promising enhanced solutions for AI-driven domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A Measurement Method for Train Wheel Size Based on Calibration Block Assisted Whole Wheel Registration 15

A Measurement Method for Train Wheel Size Based on Calibrati...

引用

15th International conference on Information Optics and Photonics, CIOP 2024

作者： xiaohui, Zhang Chaoyong, Peng Chunrong, Qiu School of Physical Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

Manual wheel size inspection faces problems such as limited accuracy, low efficiency, and poor consistency. Machine vision technology can improve measurement accuracy, efficiency and consistency through high-resolution cameras and advanced image processing algorithms. However, due to factors such as difficult railside deployment, limited camera field of view and occlusion, it becomes difficult to obtain whole wheel data, thus affecting wheel size measurements. To address this problem, this paper proposes an Iterative Closest Point (ICP) algorithm improved by sampling consistency and distance threshold decay based on the Random Sample Consensus (RANSAC) algorithm. Then, by adding calibration blocks and combining this algorithm to complete the point cloud registration of the whole wheel. Finally, based on the whole wheel data, the point set of the base point rounding and the wheel profile is extracted to complete the calculation of parameters such as wheel diameter, rim height, and rim thickness. The experimental results show that the registration algorithm in this paper improves the quality of registration by 24.311% on average compared with the original one. The measurement errors of wheel diameter, rim height, and rim thickness are only 0.5844mm, 0.2437mm, and 0.5484mm, which account for 0.0640%,0.8660%, and 1.8124% of the dimensions, respectively, and satisfy the requirements of wheel detection accuracy for trains. © 2024 SPIE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

RestoreAgent: Autonomous image Restoration Agent via Multimodal Large Language Models 38

RestoreAgent: Autonomous Image Restoration Agent via Multimo...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Chen, Haoyu Li, Wenbo Gu, Jinjin Ren, Jingjing Chen, Sixiang Ye, Tian Pei, Renjing Zhou, Kaiwen Song, Fenglong Zhu, Lei The Hong Kong University of Science and Technology Guangzhou China Huawei Noah's Ark Lab Canada The University of Sydney Australia The Hong Kong University of Science and Technology Hong Kong

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, which is time-consuming and may yield suboptimal results. All-in-one models, though capable of handling multiple tasks, typically support only a limited range and often produce overly smooth, low-fidelity outcomes due to their broad data distribution fitting. To address these challenges, we first define a new pipeline for restoring images with multiple degradations, and then introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts. Furthermore, the system's modular design facilitates the fast integration of new tasks and models. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Recognition Algorithm of Human Activity and Phone Position Based on Multi-sensor Data Fusion 22

Simultaneous Recognition Algorithm of Human Activity and Pho...

引用

4th International Symposium on Signal processing systems, SSPS 2022

作者： Ai, Da Hao, Rui Feng, Chaolin Li, Yuxuan Liu, Ying Center for Image and Information Processing Xi'an University of Posts and Telecommunications Xi'an China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9781450396103

For human activity recognition based on phone sensors, the position of the phone is an important factor of the recognition accuracy. To improve the recognition accuracy of behavioral activities and the position of the phone placed, this paper proposes a classification recognition algorithm based on accelerometer and gyroscope sensors. First, sensor data collected from seven different body positions are used as inputs to a deep stacked bidirectional long and short-term memory neural network;then the activity type and the phone position are used as labels to train the neural network for simultaneous recognition of human activity and phone position;finally, the performance of the proposed method is evaluated by cross-validation. The experimental results show that placing the phone on the waist and thigh achieves the highest recognition accuracy rate. The accuracy of the simultaneous recognition of activity and position is over 90%, which is 18% higher than existing algorithms. © 2022 ACM.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Fingerprint image Enhancement Method Based on U-Net Model 1st

Fingerprint Image Enhancement Method Based on U-Net Model

引用

1st International conference on AI-generated Content, AIGC 2023

作者： Cheng, Yu-Hsin Su, Sheng-Gui Lin, Yih-Lon Hsu, Hsiang-Chen National Yunlin University of Science and Technology Yunlin Taiwan I-Shou University Kaohsiung Taiwan

ISBN: (纸本)9789819975860

Fingerprint image enhancement is a vital image processing technology that finds applications in fingerprint identification, matching, and biometric authentication. Its objective is to improve the performance and accuracy of fingerprint identification systems by enhancing the quality, clarity, and contrast of fingerprint images while reducing noise. Traditional algorithms often require adjusting parameters based on individual fingerprint images, resulting in inconsistent enhancement results. To address this challenge, this paper proposes a deep learning-based approach for fingerprint image enhancement. Experimental results demonstrate the effectiveness of U-Net architecture with different depths and the impact of incorporating attention gate. Actually, a U-Net model with a depth of six achieves superior performance, surpassing other depths, and the inclusion of attention gate further enhances the results. The proposed method not only retains more fingerprint features but also produces smoother structures and reduces noise surrounding the fingerprint image. These findings contribute to advancing fingerprint image enhancement techniques, bolstering the accuracy and performance of fingerprint identification systems in applications such as forensic analysis and biometric authentication. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Dual-stream high-dimensional features implicit augmentation for micro-expression recognition 3

Dual-stream high-dimensional features implicit augmentation ...

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Yue, xi Wang, Wenxin Chengdu University of Information Technology Sichuan Chengdu610225 China Sichuan Prov. Eng. Technology Research Center of Support Software of Informatization Application Sichuan Chengdu610225 China

ISBN: (纸本)9781510668522

Aiming at the problem that micro-expression (ME) participants have their own expressions that interfere with microexpression recognition (MER), a dual-stream high-dimensional features implicit augmentation for MER is proposed. The innovative fusion method of adaptive face mask and Action Units (AUs) is used to reduce the interference of the participants' own expressions, and combined with optical flow information to form more focused high-dimensional spatiotemporal information. Enhance high-dimensional spatio-Temporal features through an improved implicit semantic data enhancement algorithm to alleviate the problem of small sample data. Using high-dimensional spatiotemporal information to focus the network model on ME active areas. Using a two-stream hybrid convolutional network to extract features from temporal and spatial domain information respectively to improve the representation ability of the network. The experimental results show that under the evaluation criteria of MEGC2019, UF1 and UAR are improved by 5.5% and 6.8% respectively compared with the best method. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A uniform network flow feature aggregation method based on parallel LSTM 3

A uniform network flow feature aggregation method based on p...

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Shou, xin Yin, Lihua Luo, xi Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781510668522

As the Internet continues to evolve, network attacks are becoming increasingly complex, and malicious traffic is taking on more diverse forms. A key factor in the correct identification of whether traffic is benign or malicious is the method used to extract traffic features. While researchers have investigated the extraction of robust features from raw traffic data, most have focused solely on individual packets and have not considered the semantic representations of bytes in the same location across different packets. In this paper, we propose a novel method for extracting aligned byte stream features, which involves splitting packets into several parts to extract aligned bytes. Based on this method, we propose a deep-level network-based flow feature aggregation approach for detecting malicious traffic, which uses multiple LSTMs to aggregate flow features and employs deep-level networks for detection. Extensive experimental evaluations demonstrate the effectiveness of the proposed method. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Long short-term memory

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 << < 77 78 79 80 81 82 83 84 85 86 > >>

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：