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

4,753 篇 会议
55 册 图书
11 篇 期刊文献

馆藏范围

4,819 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,161 篇 工学
- 2,819 篇 计算机科学与技术...
- 2,231 篇 软件工程
- 1,266 篇 信息与通信工程
- 762 篇 控制科学与工程
- 691 篇 生物工程
- 670 篇 光学工程
- 530 篇 电气工程
- 456 篇 生物医学工程（可授...
- 419 篇 电子科学与技术（可...
- 301 篇 网络空间安全
- 182 篇 安全科学与工程
- 163 篇 机械工程
- 160 篇 仪器科学与技术
- 159 篇 交通运输工程
- 153 篇 动力工程及工程热...
- 152 篇 化学工程与技术
- 142 篇 石油与天然气工程
1,643 篇 理学
- 709 篇 生物学
- 636 篇 物理学
- 605 篇 数学
- 242 篇 化学
- 181 篇 统计学（可授理学、...
830 篇 管理学
- 466 篇 图书情报与档案管...
- 443 篇 管理科学与工程(可...
- 119 篇 工商管理
436 篇 医学
- 365 篇 临床医学
- 248 篇 基础医学(可授医学...
- 214 篇 药学(可授医学、理...
- 108 篇 公共卫生与预防医...
125 篇 法学
- 109 篇 社会学
95 篇 农学
45 篇 经济学
44 篇 文学
33 篇 教育学
12 篇 军事学

主题

320 篇 image processing
312 篇 deep learning
232 篇 image segmentati...
191 篇 signal processin...
172 篇 image classifica...
167 篇 image enhancemen...
162 篇 computer vision
140 篇 image recognitio...
138 篇 convolutional ne...
136 篇 feature extracti...
125 篇 training
115 篇 machine learning
96 篇 computer applica...
83 篇 neural networks
83 篇 computational mo...
79 篇 visualization
78 篇 object detection
71 篇 natural language...
69 篇 semantics
68 篇 artificial intel...

机构

24 篇 chitkara univers...
17 篇 maharishi school...
15 篇 lovely professio...
15 篇 tianjin universi...
13 篇 university of ch...
12 篇 the islamic univ...
12 篇 beijing institut...
11 篇 school of comput...
11 篇 centre of interd...
10 篇 peng cheng labor...
10 篇 graphic era hill...
9 篇 national univers...
9 篇 sun yat-sen univ...
9 篇 school of comput...
9 篇 vivekananda glob...
9 篇 college of compu...
8 篇 harbin engineeri...
8 篇 school of comput...
8 篇 maharishi school...
8 篇 gla university d...

作者

13 篇 chauhan rahul
11 篇 yang yang
10 篇 anand vatsala
8 篇 kukreja vinay
8 篇 gupta rupesh
7 篇 luo xiaonan
7 篇 zhao yang
6 篇 bin wang
6 篇 pei jihong
6 篇 kumar ankit
6 篇 choudhury tanupr...
6 篇 zhu ce
6 篇 ce zhu
6 篇 kumar gaurav
6 篇 bing li
6 篇 li wei
6 篇 shin seokjoo
5 篇 gill kanwarparta...
5 篇 zhiguo cao
5 篇 nagaraj p.

语言

4,709 篇 英文
109 篇 其他
21 篇 中文
1 篇 土耳其文

检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4819 条记录，以下是4411-4420 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：

CAM loss based Network Activation Refinement for Chest X-ray image based lung disease classification

CAM loss based Network Activation Refinement for Chest X-ray...

引用

Electronics and Communication, Network and computer Technology (ECNCT), international conference on

作者： Xingfang Ai Jiangmen Polytechnic Jiangmen China

With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances in the area of medical image classification. However, most existing medical image classification methods conduct their experiments on only one public dataset. When the performance of medical image classification methods within datasets greatly improves, researchers should focus more on domain adaptation tasks that are closer to practical applications. In this work, we proposed a CAM-loss guided network activation refinement method, which employed the box-level labeled lesion mask (LM) to enforce the network to activate near the lesions. A CAM-loss is proposed to evaluate the difference between CAM and LM. The training processing minimizes the classification loss and CAM-loss at the same time, to make sure the network knows not only what, but also why. When employing network activation refinement method, all CNNs tested in this work showed better cross-domain classification performances. When employing the proposed CAM-loss, the cross-domain classification performance can be further improved. The average performance improvement was about 4% for all three testing datasets. The experimental results show that when the network is guided to activate at lesion area, the baseline networks show better both within-domain and cross-domain classification performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

image Segmentation on Gabor Filtered images using Projective Transformation

Image Segmentation on Gabor Filtered images using Projective...

引用

Recent Trends in Electronics and Communication (ICRTEC), international conference on

作者： Shashi Kant Gupta Ahmed Alemran Prabhdeep Singh Alex Khang Chandra Kumar Dixit Bhadrappa Haralayya Department of CSE Eudoxia Research University USA Physics department Medicine college-Misan university Al Amarah Iraq School of Computer Applications BBD University Lucknow UP India Department of Information Technology University of Science and Technology USA Department of Physics Dr. Shakuntala Misra National Rehabilitation University Lucknow UP India Department of MBA Lingaraj Appa Engineering College Bidar Bidar Karanataka India

In recent years, image segmentation has been applied on various images for obtaining various results. They are being deployed in numerous applications. There are lot of techniques adopted to make the image distortion free. Methods are also applied when the original image needs to be extracted from the image containing the background. Projective transformation is one of the techniques where we can apply phase correlation among the images and segment the part of the image that is needed for the next processing step. In this proposed work, we have deployed a model where an input image is given. The image is then undergone into various scaling techniques. Use of active contours and graph cut methods are embedded to obtain the segmented image. The key role of this model is to apply the Gabor filters and perform projective transformation among the image to segment the original part of the image from its background. The results are proposed by giving an output in a binary form. The work runs for a maximum of 100 iterations and for that the relaxation factor is taken as 0.5.

关键词： image segmentation Matched filters Correlation Feature extraction Market research Distortion Gabor filters

来源：评论

学校读者我要写书评

暂无评论

Employing Satellite imagery on Investigation of Convolutional Neural Network image processing and Poverty Prediction Using Keras Sequential Model

Employing Satellite Imagery on Investigation of Convolutiona...

引用

Cybernetics, Cognition and Machine Learning applications (ICCCMLA), ieee international conference on

作者： Voruganti Naresh Kumar Mahesh V Sonth Arfa Mahvish Vijaya kumar Koppula B Anuradha L Chandrasekhar Reddy Department of CSE CMR Technical Campus Hyderabad Telangana India Department of ECE CMR Technical Campus Hyderabad Telangana India Department of IT CMR Technical Campus Hyderabad Telangana India Department of CSE CMR College of Engineering & Technology Hyderabad Telangana India Department of CSD CMR College of Engineering & Technology Hyderabad Telangana India

The primary objective of the proposed model is that the most of the world's poorest individuals reside in areas where national domestic assessments are used to gather info on deficiency. It is difficult to acquire current and precise data because it takes a lot of assets to conduct these surveys. Due to advancements in computer vision and the widespread availability of abundant data sources such as satellite images captured during daylight and nocturnal lighting, a practical solution to the problem of data scarcity is now feasible. This study is going to expand on previous research by processing daytime satellite photos and nighttime lights employing machine learning techniques for the purpose predict, the distribution of poverty at the local level in countries utilizing new technology and modern data sources. Innovative and fascinating possibilities, such as the detailed classification of specific objects on a per-pixel basis, have become feasible due to the availability of aerial satellite data. This study demonstrates the efficacy of a convolutional neural network (CNN) in efficiently and accurately classifying individual pixels inside satellite imagery of a compact urban area. The broad segmentation is then refined by incorporating the expected detailed pixel classifications, enhancing the overall accuracy and speed of the classification process. Examined and assessed are the several architectural decisions made for the CNN architecture. The five different types of terrain, ground cover, roads, structures, and water are all physically categorized and assigned to the study area's land mass. The correctness of classification is compared with other per-pixel classification methods for contrast tests conducted on different terrain areas with a comparable number of categories. Convolutional Neural Networks (CNNs) have demonstrated their efficacy in effectively addressing the task of segmenting and detecting objects in remote sensing data. This is clear from the compl

关键词：

来源：评论

学校读者我要写书评

暂无评论

image Recognition and Enhancement using Multi Scale Retinex and Histogram Equalization

Image Recognition and Enhancement using Multi Scale Retinex ...

引用

Innovation in Technology (INOCON), ieee international conference for

作者： K R Tejas B. Rajalakhmi Palle Geethika C Indrani Ramya Swathi Kolalapudi CSE NHCE Bangalore India

nowadays the need for a safe and at ease device is favored using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output

关键词： Histograms Technological innovation Rain image recognition image color analysis Surveillance Mobile applications

来源：评论

学校读者我要写书评

暂无评论

image Denoising using Autoencoders : Denoising noisy imgaes by removing noisy pixels/grains from natural images using Deep learning and autoencoders techniques

Image Denoising using Autoencoders : Denoising noisy imgaes ...

引用

international conference for Convergence of Technology (I2CT)

作者： Uday Kulkarni Sachin Patil Vikas E Rahul Patil Bodha Kulkarni Meena S. M Akshay Shanbhag School of Computer Science KLE Technological University Hubli India

Denoising images is widely used in applications from critical medical systems to software based image enhancement in our cell phones. The natural noise is simulated by adding noise to an image in random pixels. Currently the denoising problem can be solved in either with greedy algorithm or by deep learning techniques. In this paper we are discussing the usage of Autoencoders a deep learning technique to solve the problem of denoising. Autoencoders use down-sampling and up-sampling techniques to remove the unwanted noise from an *** using Encoders we compress the image gradually to remove the tiny details like noise using convolution layers and ReLU as activation function. The compressed images are fed to Decoders, the decoder up-samples the image bit-by-bit till the image reaches the original resolution, while up-sampling the tiny details like human hair or tiny speckles on face are restored without trying to bring back the noisy pixels. To train the proposed model we used FFHQ-Face dataset which consists of 70 thousand images of 128x128 resolution, each image is a face of and unique individual. The output of the neural network is a denoised image with PSNR of above 35, which maximum achieved compared to other pre-trained models.

关键词： Deep learning image resolution image coding Convolution Noise reduction Neural networks Software

来源：评论

学校读者我要写书评

暂无评论

An Automatic Method for Sublingual image Segmentation and Color Analysis

An Automatic Method for Sublingual Image Segmentation and Co...

引用

Annual international conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Zhecheng Yang Hongyu Gu Hong Chen School of Integrated Circuits Tsinghua University Beijing China

Automatic segmentation of sublingual images and color quantification of sublingual vein are of great significance to disease diagnosis in traditional Chinese medicine. With the development of computer vision, automatic sublingual image processing provides a noninvasive way to observe patients’ tongue and is convenient for both doctors and patients. However, current sublingual image segmentation methods are not accurate enough. Besides, the differences in subjective judgments by different doctors bring more difficulties in color analysis of sublingual veins. In this paper, we propose a method of sublingual image segmentation based on a modified UNet++ network to improve the segmentation accuracy, a color classification approach based on triplet network, and a color quantization method of sublingual vein based on linear discriminant analysis to provide intuitive one-dimensional results. Our methods achieve 88.2% mean intersection over union (mIoU) and 94.1% pixel accuracy on tongue dorsum segmentation, and achieves 69.8% mIoU and 82.7% pixel accuracy on sublingual vein segmentation. Compared with the state-of-art methods, the segmentation mIoUs are improved by 5.8% and 5.3% respectively. Our sublingual vein color classification method has the highest overall accuracy of 81.2% and the highest recall for the minority class of 77.5%, and the accuracy of color quantization is 90.5%.Clinical Relevance— The methods provide accurate and quantified information of the sublingual image, which can assist doctors in diagnosis.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mushroom Classification Based on Deep Residual Network

Mushroom Classification Based on Deep Residual Network

引用

Pattern Recognition and Machine Learning (PRML), ieee international conference on

作者： Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. Then, a transfer learning strategy is employed to initialize the network parameters from a pre-trained model, followed by fine-tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. This method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Survey of applications for Anomaly Detection in the IoT: Methods, New Perspectives, and Future

A Survey of Applications for Anomaly Detection in the IoT: M...

引用

ieee international conference on Systems, Man and Cybernetics

作者： Yingxiang Wang Rongzuo Guo Peng Min College of Computer Science Sichuan Normal University Chengdu Sichuan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, with the rapid increase the popularity of cellular Internet of Things (IoT) devices and the sharp increase in the number of end users, ensuring the stability and reliability of IoT systems has become an important challenge. In this context, anomaly detection techniques provide solutions for IoT applications. This paper reviews anomaly detection research applied in the field of the IoT in recent years (mainly from 2018 to 2023) from a technical perspective. First, the causes and basic types of IoT anomalies are introduced to provide a better understanding of the importance of anomaly detection. Second, we focus on research progress in machine learning and edge computing, and propose a general workflow for anomaly detection in the IoT based on edge computing, which we call ECADW. Furthermore, the challenges of anomaly detection in the IoT are proposed, and future research per-spectives are prospected. It is hoped that this review can help researchers to better understand the research direction of this topic and choose interesting anomaly detection techniques.

关键词： Surveys Reviews image edge detection Machine learning Internet of Things Reliability Anomaly detection Cybernetics Edge computing

来源：评论

学校读者我要写书评

暂无评论

Identifying image Modifications using DCT and JPEG Quantization Technique

Identifying Image Modifications using DCT and JPEG Quantizat...

引用

international conference for Convergence of Technology (I2CT)

作者： Prajakta Kubal Namita Pulgam Vanita Mane Department of Computer Engineering Ramrao Adik Institution of Technology D Y Patil Deemed to be University Navi Mumbai India

The volume of images and the videos are being shared on various social media platform are huge and there are cybercrimes happening in these areas. Hence, identification of the main source of social network, based on the images uploaded or downloaded from social network has become an important activity in the multimedia forensic analysis. When media shared on social network there are possibilities of exploiting different pattern embedding in image content by social network. To make it easier system is proposed with discrete cosine transform (DCT) method and JPEG Quantization to identify the changes in shared images on social networks applications. The quantization used in JPEG compression is used to help in separating the images that have been processed by software. DCT finds the pixel value of the blur images and it is easier in implementation. The combination of DCT and JPEG quantization can give the better accuracy rate which helps in finding the images shared or the source of the images.

关键词： Visualization Quantization (signal) Social networking (online) Transform coding Streaming media Metadata Software

来源：评论

学校读者我要写书评

暂无评论

LVOS: A Benchmark for Long-term Video Object Segmentation

LVOS: A Benchmark for Long-term Video Object Segmentation

引用

international conference on computer Vision (ICCV)

作者： Lingyi Hong Wenchao Chen Zhongying Liu Wei Zhang Pinxue Guo Zhaoyu Chen Wenqiang Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Academy for Engineering and Technology Fudan University

Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the application in realistic scenarios. So, in this paper, we present a new benchmark dataset named LVOS, which consists of 220 videos with a total duration of 421 minutes. To the best of our knowledge, LVOS is the first densely annotated long-term VOS dataset. The videos in our LVOS last 1.59 minutes on average, which is 20 times longer than videos in existing VOS datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts. Based on LVOS, we assess existing video object segmentation algorithms and propose a Diverse Dynamic Memory network (DDMemory) that consists of three complementary memory banks to exploit temporal information adequately. The experimental results demonstrate the strength and weaknesses of prior methods, pointing promising directions for further study. Data and code are available at https://***/***/.

关键词：

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482页 << < 438 439 440 441 442 443 444 445 446 447 > >>

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：