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
364 篇 期刊文献
193 册 图书

馆藏范围

16,444 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,342 篇 工学
- 5,434 篇 计算机科学与技术...
- 2,999 篇 软件工程
- 2,166 篇 电气工程
- 1,868 篇 信息与通信工程
- 1,128 篇 控制科学与工程
- 890 篇 光学工程
- 647 篇 机械工程
- 642 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
- 203 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
4,016 篇 理学
- 2,399 篇 物理学
- 1,361 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...
1,055 篇 管理学
- 613 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
44 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...
1,511 篇 signal processin...
868 篇 feature extracti...
656 篇 image edge detec...
636 篇 cameras
633 篇 signal processin...
611 篇 real-time system...
579 篇 image reconstruc...
519 篇 image coding
498 篇 algorithm design...
401 篇 deep learning
399 篇 clustering algor...
398 篇 image color anal...
383 篇 object detection
370 篇 image enhancemen...
357 篇 accuracy
334 篇 hardware
323 篇 image analysis
312 篇 computer vision

机构

40 篇 xi'an jiaotong u...
32 篇 northwestern pol...
31 篇 xi an jiao tong ...
30 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

16,020 篇 英文
279 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 16444 条记录，以下是1571-1580 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：

Low-illumination image Enhancement Method Based on Adaptive MSRCR Algorithm 7

Low-illumination Image Enhancement Method Based on Adaptive ...

引用

7th International conference on Computational Linguistics and Intelligent systems. Volume I: Machine Learning Workshop, CoLInS 2023

作者： Ning, xiongwei Su, Jun Kochan, Orest Hubei University of Technology No.28 Street NanLi Wuhan430068 China Lviv Polytechnic National University 12 S. Bandera Str. Lviv Ukraine

The low-illumination image enhancement method based on the adaptive MSRCR algorithm is proposed to address the problems of the Retinex algorithm in processing low-illumination images, such as the need to manually adjust parameters and blurred details. In the HSV color space of the original image, the luminance V component is decomposed by mean filtering to create a detail layer, and the detail layer information is enhanced by using enhancement weights. The improved Salp Swarm Algorithm (LLSSA) is proposed for adaptive parameter adjustment of Multi-Scale Retinex with Colour Recovery (MSRCR) and detail layer weights, which uses Logistic Chaos to initialise the salps population and introduces Lévy flights into the updated positions of leaders and followers to enhance the global search capability. Finally, the adaptive MSRCR enhancement map and the detail layer enhancement map are images fused to produce a final enhanced image with clear details. The experimental results show that compared with several typical algorithms, the algorithm in this paper can effectively maintain the image details, improve the image brightness and have better visual effects. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

SA3DIP: Segment Any 3D Instance with Potential 3D Priors 38

SA3DIP: Segment Any 3D Instance with Potential 3D Priors

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Yang, xi Gu, Xu Yin, xingyilang Gao, xinbo Xidian University China Chongqing University of Posts and Telecommunications China

The proliferation of 2D foundation models has sparked research into adapting them for open-world 3D instance segmentation. Recent methods introduce a paradigm that leverages superpoints as geometric primitives and incorporates 2D multi-view masks from Segment Anything model (SAM) as merging guidance, achieving outstanding zero-shot instance segmentation results. However, the limited use of 3D priors restricts the segmentation performance. Previous methods calculate the 3D superpoints solely based on estimated normal from spatial coordinates, resulting in under-segmentation for instances with similar geometry. Besides, the heavy reliance on SAM and hand-crafted algorithms in 2D space suffers from over-segmentation due to SAM's inherent part-level segmentation tendency. To address these issues, we propose SA3DIP, a novel method for Segmenting Any 3D Instances via exploiting potential 3D Priors. Specifically, on one hand, we generate complementary 3D primitives based on both geometric and textural priors, which reduces the initial errors that accumulate in subsequent procedures. On the other hand, we introduce supplemental constraints from the 3D space by using a 3D detector to guide a further merging process. Furthermore, we notice a considerable portion of low-quality ground truth annotations in ScanNetV2 benchmark, which affect the fair evaluations. Thus, we present ScanNetV2-INS with complete ground truth labels and supplement additional instances for 3D class-agnostic instance segmentation. Experimental evaluations on various 2D-3D datasets demonstrate the effectiveness and robustness of our approach. Our code and proposed ScanNetV2-INS dataset are available HERE. © 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

Color image segmentation based on thresholding for advanced driver assistance systems

Color image segmentation based on thresholding for advanced ...

引用

IEEE Zooming Innovation in Consumer Technologies conference (ZINC)

作者： Budak, Luka Grbic, Ratko Cetic, Nenad Kastelan, Ivan TTTech Auto CEE Cara Hadrijana 10b Osijek Croatia Fac Elect Engn Comp Sci & Informat Technol Kneza Trpimira 2b Osijek Croatia Res & Dev Inst RT RK Narodnog Fronta 23A Novi Sad Serbia Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad Serbia

ISBN: (纸本)9781665483759;9781665483742

Many Advanced Driver Assistance systems (ADASs) rely on an image obtained by a camera that is mounted on a vehicle. To get useful information in real-time, the acquired image is processed with different computer vision algorithms running on the vehicle's embedded platform. The common preprocessing task is the image segmentation based on color which is often used in lane detection or traffic light/sign recognition algorithms to extract key regions. In this paper, we focus on a color image segmentation based on thresholding. While being very simple, its effectiveness largely depends on the details of the implementation such as the chosen color space or characteristics of the used embedded platform. We provide details regarding PC implementation and ADAS development board implementation as well as the details regarding optimizations that are carried out to achieve smaller execution time on PC and board's Texas Instruments TDA2xx System-On-Chip. The image segmentation mean processing time is reported for three different resolutions and three different color models (RGB, HSV, YUV) for both PC and ADAS development board. The obtained results can help in planning and allocating resources on the vehicle's embedded platform for such computer vision tasks.

关键词： ADAS image segmentation image thresholding VisionSDK TDA2xx

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of High-speed and High-precision CORDIC Algorithm

Design and Implementation of High-speed and High-precision C...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International conference on

作者： Zhenfu Feng Hao Chang Huan Zhang Ang Li Lidong xing Xi'an University of Posts and Telecommunications School of Electronic Engineering Xi an China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

For the traditional coordinate rotation digital computer (CORDIC) algorithm in the implementation of trigonometric function calculation there are problems of low output accuracy and long output delay, a low-latency CORDIC algorithm using the greedy algorithm to eliminate redundant iterations is proposed, which analyses the concomitantly induced unfixed scaling factor, and corrects the scaling factor by using binary decomposition, which ensures the computational Accuracy. The simulation analysis is carried out in MATLAB, and the implementation and verification are carried out on field programmable gate array (FPGA); the results show that, for example, with 32-bit output, the average number of iterations in the calculation of trigonometric functions has been reduced by 65.6%, and the average accuracy of the calculation results reaches 3.46E-11, so the algorithm has potential application value in digital signal processing systems with high real-time and high accuracy requirements.

关键词： Greedy algorithms Digital computers Accuracy Signal processing algorithms Real-time systems Delays Registers MATLAB Field programmable gate arrays Arithmetic

来源：评论

学校读者我要写书评

暂无评论

CAU: A Consensus Model of Augmented Unlabeled Data for Medical image Segmentation 7

CAU: A Consensus Model of Augmented Unlabeled Data for Medic...

引用

7th International conference on image, Vision and Computing, ICIVC 2022

作者： Cheng, Wenli Jiao, Jiajia Shanghai Maritime University College of Information Engineering China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Medical image segmentation plays an important role in medical diagnosis and treatment. However, medical image data are more expensive and time-consuming to obtain than ordinary image data. In this paper, we propose a novel semi-supervised method named CAU for medical image segmentation, which can easily use Convolutional Neural Networks (CNNs) to segment 2D images. The network learns through a combination of common supervision losses for labeled data and losses for unlabeled data. Specifically, we augment the unlabeled data strongly and weakly and send them to the student model and the teacher model respectively. We take full advantage of unlabeled data learning through a novel combination of minimizing the difference between network predictions for different data augmentation processing scenarios and using an unsupervised loss of min-entropy on the outputs of the two networks. In order to improve the regularization effect, we use the teacher-student model to optimize the teacher model by averaging the student model weights. Experiments show that our method in labeled data experiments with 5%, 10%,35% and 50% labeled data on Automated Cardiac Diagnosis Challeng(ACDC) dataset exceeds the fully supervised algorithm using the same amount of data and the existing popular semi-supervised learning algorithms 1.922% ~ 4.451%(Dice),0.841 ~ 5.031(Asd) and 0.06 ~ 1.116(HD95), respectively, and the Dice index exceeds the fully supervised algorithm 0.019% with 50% labeled data, which verifies its effectiveness in medical image segmentation. © 2022 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark 38

WebUOT-1M: Advancing Deep Underwater Object Tracking with A ...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhang, Chunhui Liu, Li Huang, Guanjie Wen, Hao Zhou, xi Wang, Yanfeng Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China Guangzhou511458 China CloudWalk Technology Co. Ltd. 201203 China Shanghai AI Laboratory Shanghai200232 China

Underwater Object Tracking (UOT) is essential for identifying and tracking submerged objects in underwater videos, but existing datasets are limited in scale, diversity of target categories and scenarios covered, impeding the development of advanced tracking algorithms. To bridge this gap, we take the first step and introduce WebUOT-1M, i.e., the largest public UOT benchmark to date, sourced from complex and realistic underwater environments. It comprises 1.1 million frames across 1,500 video clips filtered from 408 target categories, largely surpassing previous UOT datasets, e.g., UVOT400. Through meticulous manual annotation and verification, we provide high-quality bounding boxes for underwater targets. Additionally, WebUOT-1M includes language prompts for video sequences, expanding its application areas, e.g., underwater vision-language tracking. Given that most existing trackers are designed for open-air conditions and perform poorly in underwater environments due to domain gaps, we propose a novel framework that uses omni-knowledge distillation to train a student Transformer model effectively. To the best of our knowledge, this framework is the first to effectively transfer open-air domain knowledge to the UOT model through knowledge distillation, as demonstrated by results on both existing UOT datasets and the newly proposed WebUOT-1M. We have thoroughly tested WebUOT-1M with 30 deep trackers, showcasing its potential as a benchmark for future UOT research. The complete dataset, along with codes and tracking results, are publicly accessible at here. © 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

Generalizable Real-time Accelerated Dynamic MRI

Generalizable Real-time Accelerated Dynamic MRI

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Silpa Babu Wahidul Alam Rushdi Zahid Rusho Sajan Goud Lingala Namrata Vaswani ECE Dept Iowa State University USA BME Dept University of Iowa USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We introduce a real-time undersampled dynamic MRI algorithm, termed FewShot-AltGDmin-MRI, that is generalizable: works for many different applications and sampling trajectories without any application-specific parameter tuning. FS-AGM-MRI operates in real-time after processing the first short mini-batch, i.e., it can provide a reconstruction of each new image frame as soon as the MRI scan data for that frame arrives. It also provides a second set of improved quality reconstructions after a short delay. We compare our algorithm against many state of the art batch MRI algorithms, including Deep Learning (DL) based ones, on 17 different retrospectively undersampled datasets and two prospective datasets. FS-AGM-MRI is the only approach that provides accurate recovery for all datasets while also being one of the fastest.

关键词： Magnetic resonance imaging Heuristic algorithms Signal processing algorithms Signal processing Real-time systems Trajectory Delays Speech processing image reconstruction Tuning

来源：评论

学校读者我要写书评

暂无评论

image Classification With The Hybrid PVCN 9

Image Classification With The Hybrid PVCN

引用

9th IEEE International conference on Smart Structures and systems, ICSSS 2023

作者： Pranaya, B. Kumar, A. Vinay Pruthviraj, C. Gangothri, N. Chatterjee, Priyadarshini B v Raju Institute of Technology Computer Science and Engineering Telangana Gajwel India B v Raju Institute of Technology Computer Science and Engineering Telangana Kodad India B v Raju Institute of Technology Computer Science and Engineering Telangana Sangareddy India B v Raju Institute of Technology Computer Science and Engineering Telangana Medak India B v Raju Institute of Technology Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350384208

Our research introduces an innovative methodology that seamlessly integrates ResNet-50, an advanced Convolutional Neural Network (CNN), with the HalfUNet architecture, enriched by the attention-gate mechanism, to address the complex task of classifying color and black-and-white images with exceptional precision. Our primary objective is to achieve highly accurate classification while effectively discerning between color and black-and-white images. At the core of our approach lies meticulous data preprocessing, incorporating advanced denoising techniques that substantially enhance the quality of our dataset, yielding an impressive Peak Signal-to-Noise Ratio (PSNR) of 71.35%. This PSNR score substantiates the superior quality of our refined dataset and ensures the consistent performance of our model. Leveraging state-of-the-art deep learning techniques and harnessing the unique capabilities of the Half-UNet architecture, particularly its image classification process, enhanced by the attention-gate mechanism, our approach attains an outstanding image classification accuracy rate of 91.3%. This remarkable achievement underscores the robustness and adaptability of our model, enabling it to perform effectively even when confronted with unfamiliar colorization algorithms during testing and limited prior training data. Our research offers an innovative solution that combines ResNet-50 with Half-UNet, incorporating with 1000 images of rigorous dataset pre-processing, quality assessment, and the attention-gate mechanism to achieve remarkable accuracy in image categorization and colorization tasks. © 2023 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Research on Improved Joint Denoising and Demosaicing algorithms

Research on Improved Joint Denoising and Demosaicing Algorit...

引用

IEEE Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Lin Fu Mingxuan Zhang Jie Guo School of Physics and Electronic Information Yunnan Normal University Kunming China Xi'an Institute of Applied Optics Xian China

In the process of image processing, image denoising and image demosaicing have been the research hotspots and problems. Denoising and demosaicing are two adjacent important modules in image Signal processing (ISP). The existing traditional joint denoising and demosaicing algorithms have problems such as poor image quality and high complexity. This paper proposes a joint denoising and demosaicing algorithm that combines the wavelet threshold denoising algorithm and the nonlocal means (NLM) algorithm. The experiments show that this method has some improvement in time efficiency and objective numerical peak signal-to-noise ratio (PSNR)/Structure Similarity Index Measure (SSIM) metrics under different levels of noise compared to two traditional algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Blurred image processing with IoT Integration for Improved Clarity

Enhancing Blurred Image Processing with IoT Integration for ...

引用

Emerging Research in Computational Science (ICERCS), International conference on

作者： Geetamma Tummalapalli Summia Parveen H Ramesh S M Monica P Santhiya B Arun Sekar Rajasekaran Department of Electronics and Communication Engineering GMR Institute of Technology Rajam India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering GST GITAM Deemed to be University Visakhapatnam India Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering SR University Warangal India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains.

关键词： image sensors Cloud computing Privacy image processing image edge detection Noise Visual effects Real-time systems Internet of Things Object 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 << < 154 155 156 157 158 159 160 161 162 163 > >>

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：