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

1,778 篇 会议
14 册 图书
11 篇 期刊文献

馆藏范围

1,803 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,386 篇 工学
- 1,114 篇 计算机科学与技术...
- 818 篇 软件工程
- 457 篇 信息与通信工程
- 331 篇 光学工程
- 303 篇 控制科学与工程
- 199 篇 电气工程
- 181 篇 生物工程
- 152 篇 机械工程
- 126 篇 生物医学工程（可授...
- 97 篇 电子科学与技术（可...
- 85 篇 仪器科学与技术
- 65 篇 安全科学与工程
- 60 篇 化学工程与技术
- 42 篇 材料科学与工程（可...
- 40 篇 交通运输工程
- 25 篇 网络空间安全
615 篇 理学
- 373 篇 物理学
- 194 篇 数学
- 192 篇 生物学
- 61 篇 统计学（可授理学、...
- 58 篇 化学
- 25 篇 系统科学
222 篇 管理学
- 129 篇 管理科学与工程(可...
- 107 篇 图书情报与档案管...
- 46 篇 工商管理
136 篇 医学
- 125 篇 临床医学
- 58 篇 基础医学(可授医学...
- 48 篇 药学(可授医学、理...
37 篇 农学
- 33 篇 作物学
25 篇 艺术学
- 25 篇 设计学（可授艺术学...
20 篇 教育学
18 篇 法学
15 篇 经济学
4 篇 军事学
1 篇 文学

主题

289 篇 computer vision
164 篇 image processing
106 篇 feature extracti...
99 篇 deep learning
99 篇 image segmentati...
50 篇 object detection
47 篇 machine vision
47 篇 computational mo...
47 篇 convolutional ne...
45 篇 image enhancemen...
44 篇 image recognitio...
43 篇 cameras
37 篇 accuracy
36 篇 image edge detec...
36 篇 face recognition
34 篇 machine learning
33 篇 image color anal...
33 篇 training
31 篇 image reconstruc...
30 篇 visualization

机构

6 篇 shenyang institu...
6 篇 university of ch...
6 篇 shenyang ligong ...
5 篇 department of co...
4 篇 key laboratory o...
4 篇 school of softwa...
4 篇 school of inform...
4 篇 department of co...
4 篇 college of compu...
4 篇 beijing informat...
3 篇 school of comput...
3 篇 shanghaitech uni...
3 篇 chitkara univers...
3 篇 university of el...
3 篇 school of comput...
3 篇 institute for co...
3 篇 guangzhou civil ...
3 篇 institutes for r...
3 篇 babol noshirvani...
3 篇 computer vision ...

作者

5 篇 raman balasubram...
5 篇 lu zongqing
4 篇 zhang chao
4 篇 balasubramanian ...
4 篇 ramani sanjeev k...
4 篇 wang wei
4 篇 hossain syed akh...
4 篇 iyengar s.s.
4 篇 sanjeev kaushik ...
3 篇 liu bo
3 篇 kumar krishan
3 篇 zhang ji
3 篇 elizabeth rani g...
3 篇 neehal nafis
3 篇 rabby akm shahar...
3 篇 luc van gool
3 篇 farag aly a.
3 篇 zhao jiewen
3 篇 zou xiaobo
3 篇 zhang hualiang

语言

1,721 篇 英文
65 篇 其他
19 篇 中文
1 篇 土耳其文

检索条件"任意字段=2nd International Conference on Computer Vision and Image Processing, CVIP 2017"

共 1803 条记录，以下是911-920 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：

Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine vision 2nd

Automatic Classification of Normal and Affected Vegetables B...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Madgi, Manohar Danti, Ajit K. L. E. Institute of Technology Hubballi580030 India Bengaluru560074 India

ISBN: (纸本)9789811391866

This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Backpropagation

来源：评论

学校读者我要写书评

暂无评论

Skeleton Joint Difference Maps for 3D Action Recognition with Convolutional Neural Networks 2nd

Skeleton Joint Difference Maps for 3D Action Recognition wit...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Naveenkumar, M. Domnic, S. Department of Computer Applications National Institute of Technology Trichy India

ISBN: (纸本)9789811391804

Action recognition is a leading research topic in the field of computer vision. This paper proposes an effective method for action recognition task based on the skeleton data. Four features are proposed based on the joint differences from 3D skeleton data. From the differences of 3D coordinates of corresponding joints in successive frames, three maps are extracted related to x, y and z coordinates respectively and then these maps are encoded into 2D color images, named as Joint Difference Maps (JDMs). The fourth JDM is formed by mapping the individual x, y and z difference maps into red, green and blue values. Hence, the 3D action recognition problem is converted into 2D image classification problem. It enables us to fine tune CNNs to learn informative features for 3D action recognition problem. The proposed method achieved 79.30% recognition rate on UTD MHAD dataset. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Automatic Multiclass Classification of Foliar Leaf Diseases Using Statistical and Color Feature Extraction and Support Vector Machine 2nd

Automatic Multiclass Classification of Foliar Leaf Diseases ...

引用

2nd international conference on Computational Intelligence, Communication, and Business Analytics, CICBA 2018

作者： Datta, Aparajita Dey, Abhishek Dey, Kashi Nath Department of Computer Science and Engineering University of Calcutta Kolkata India Department of Computer Science Bethune College University of Calcutta Kolkata India Department of Computer Science and Engineering DSCSDEC JIS GROUP Kolkata India

ISBN: (纸本)9789811385773

Disease detection in crops and plants is essential for production of good and improved quality of food, life and a stable agricultural economy. It becomes tedious and time consuming to observe the infected parts of plants manually. Recourses with proper expertise are also required to have continuous monitoring. Digital image processing along with computer vision techniques can be applied to automate early detection of plant diseases and it can save significant amount of resources. In this paper, an automated approach based on image processing and machine learning techniques is proposed which can detect three major kinds of diseases Downy Mildew, Frogeye Leaf Spot and Septoria Leaf Blight that affects apple, grapes, soybean, tomatoes and many other major plants of economic value. Generally, leaves are the most affected part of the plants. So, instead of the whole plant, concentration is given on the leaf. In this paper, image pre-processing methods like noise removal and contrast enhancement followed by colour space transformation and k-means clustering is used to segment affected parts of soybean leaves, after that both texture and colour features are extracted from segmented samples and Support Vector Machine (SVM) classification is used to separate three kinds of diseases mentioned previously. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Can We Understand image Semantics from Conventional Neural Networks? 2nd

Can We Understand Image Semantics from Conventional Neural N...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Do, Thanh-Ha Anh, Nguyen T. V. Dat, Nguyen T. Santosh, K.C. Department of Informatics VNU University of Science Hanoi Viet Nam FPT Technology Research Institute Hanoi Hanoi Viet Nam Department of Computer Science University of South Dakota Vermillion United States

ISBN: (纸本)9789811391804

The Convolutional Neural Networks (CNNs) have been employed successfully for object identification, behavior analysis, letters and digits recognition, etc. The researchers in computer vision committee have studied the capacity of this model in two directions. The first one is improving its performance by increasing layers of the network, using learned features, more data, or more computing (GPUs). The second one is theoretical understanding in architecture design, in optimization and in a generalization of deep networks. One of the first researches in limitation of CNNs in understudying the semantics of images has done by Hosseini et al. in 2017. This result puts the researchers in CNNs community to do more researches to keep up with proper understanding and continued advances in the fields. This paper also forces on analysis the CNNs capability in understanding semantics information in images by recognizing images of the same shape and semantics but opposite the intensity. Experimental results were done on MNIST and GTSRB dataset indicates the limitation of CNNs in understanding the image semantic. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

HOG, LBP and SVM based Traffic Density Estimation at Intersection 2

HOG, LBP and SVM based Traffic Density Estimation at Interse...

引用

2nd IEEE-Pune-Section Annual international conference (IEEE PuneCon)

作者： Prasad, Devashish Kapadni, Kshitij Gadpal, Ayan Visave, Manish Sultanpure, Kavita Pune Inst Comp Technol Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9781728119243

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and might run efficiently on raspberry pi board.

关键词： image processing computer vision Traffic estimation Traffic density HOG Histogram of Oriented Gradients LBP Local Binary Patterns Smart traffic junction Traffic intersection Intelligent traffic control system Machine learning SVM Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Refinenet4dehaze: Single image dehazing network based on refinenet 1

引用

2nd Chinese conference on Pattern Recognition and computer vision, PRCV 2019

作者： Ma, Kuan Feng, Hongwei Luo, Jie Bo, Qirong Northwestern University Xi’anShaanxi China

ISBN: (数字)9783030317232

ISBN: (纸本)9783030317225

In recent years, the image dehazing technology with deep learning has been updated rapidly. These deep learning method shows more potential compared with traditional methods which need to estimate the atmospheric scattering model and prior information. In this paper, we proposed an end-to-end multi-scale single image dehazing method, and the method does not rely on the parameter estimation of atmospheric scattering model. We use the residual network (ResNet) to extract the residual features of hazy images at different scales, and then we use a kind of enhanced RefineNet to fuse these features. Finally we use recursively network to directly learn the nonlinear mapping from hazy images to haze-free images. We assessed our method on two datasets, and compared with two representative and effective methods. The results demonstrated that the proposed method has better performance on image dehazing. © Springer Nature Switzerland AG 2019.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Infrared image Pedestrian Detection Techniques with Quantitative Analysis 2nd

Infrared Image Pedestrian Detection Techniques with Quantita...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Soundrapandiyan, Rajkumar Santosh, K.C. Chandra Mouli, P.V.S.S.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Computer Science University of South Dakota Vermillion United States Department of Computer Applications NIT Jamshedpur Jamshedpur India

ISBN: (纸本)9789811391866

Pedestrian detection in infrared (IR) images is important due to widely used IR images in many applications including surveillance, night vision, searching, environmental monitoring, driving assistant system etc. Among these pedestrian detection in defense gained more attention in the infrared images. However, there are still many problems existed in pedestrian detection in infrared images are low signal to noise ratio, low contrast, complex background, pedestrians are prone to occluded by other things and lack of shape. In this paper, Global background subtraction, adaptive filter and local adaptive thresholding based Pedestrian Detection method proposed to overcome these problems. Further, the proposed method tested on the OSU thermal pedestrian database. In addition, proposed method result is compared along with the popular existing traditional methods using quantitative measures. From experimental results deduced that the proposed method earned excellent detection rate when compared to other methods. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Adaptive filters

来源：评论

学校读者我要写书评

暂无评论

DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep Learning with Multi Support Vector Machine for Classification 2nd

DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep...

引用

2nd international conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2018

作者： Bhatti, Emmy Kaur, Prabhpreet Computer Engineering and Technology Guru Nanak Dev University Amritsar India

ISBN: (纸本)9789811391835

Diabetic retinopathy (DR) is the leading cause of avertable blindness globally. Retinal scanning of eyes is critical for examining the disease at an early stage. The concern of this study is to develop a robust mechanism to automate the process of diabetic retinopathy detection. A person suffering from DR must be referred to an ophthalmologist for an early evaluation in order to reduce the rate of vision loss hence enabling early treatment and prevention of vision loss. The proposed methodology introduces a data-driven novel algorithm using Deep learning for creating a tool for detecting DR. The approach uses DR colored fundus images and classifies them into multiple classes as stages or levels to which the eye is infected. Set of 170 colored fundus images of diabetic patients were used to train and test the model for distinguishing images into multiple classes. Entire simulation is divided into primarily two phases-firstly, the preprocessing phase where the resizing operation is performed since the input layer of the network requires predefined size. Size of 77 × 100 and channel of size 3 is used, indicating RGB image. Gaussian Filtering is used to tackle noise (De-noising) if any within the image. Secondly, the MSVM for segmentation and Classification phase where Multi-class SVM is used to extract critical and non-critical segments from within the training images. After extracting the features, classification is performed on the test images. Parameters used for optimization includes Accuracy, sensitivity, and specificity. Simulation is conducted in MATLAB using image processing and neural network toolbox. The proposed mechanism shows improvement in terms of classification accuracy by the margin of 5–6% which is significant, thereby enhancing the recognition rate. © Springer Nature Singapore Pte Ltd. 2019.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

A real-time monitoring and control system for crop 2

A real-time monitoring and control system for crop

引用

2nd international conference on Algorithms, Computing and Artificial Intelligence, ACAI 2019

作者： Feng, Tianyu Chai, Yueting Huang, Yadong Liu, Yi National Engineering Laboratory for E-Commerce Technologies Department of Automation Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9781450372619

The current production of agricultural products has some problems, such as poor customized service, difficulty in quality control and high wastage in production process. Based on the technology of Internet of Things (IoT), artificial intelligence and blockchains, this paper designs an smart precision agricultural system called Agricultural computer, which can realize the holographic, automatic and intelligent production of agricultural products. The system can solve the separation of information collection, processing and application in agricultural production, so it can supplement corresponding nutrients according to the physiological needs of plants. The system realizes the standardization, precision and intelligence of agricultural production, improves the quality of agricultural products, promotes the greening and high quality of agricultural production, and meets the needs of consumers for personalized and healthy consumption. © 2019 ACM.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Generation of photographic mosaic using apache spark and scalding for image processing 2nd

Generation of photographic mosaic using apache spark and sca...

引用

2nd international conference on Microelectronics, Computing and Communication Systems, MCCS 2017

作者： Neralla, Santhoshi Rupa Gayatri Electrical and Computer Engineering Sungkyunkwan University Suwon-Si Korea Republic of

ISBN: (纸本)9789811082337

Spark is an open-source big data processing framework which is one of the emerging platforms. Spark can be employed to process large datasets in distributed environment. Spark has a programming model which is similar to MapReduce that expands itself with a property of data sharing abstraction called resilient distributed datasets. This paper illustrates how an Apache Spark framework is most effectively used as an incisive solution for addressing the big data image-processing problem of generating mosaics with the broad range of libraries inbuilt in it. In this paper, we evaluated the performance of Apache Spark using image-processing technique and compared its performance with Scalding. The results imply that for larger datasets Spark runs 17× times faster than Scalding. © Springer Nature Singapore Pte Ltd. 2019.

关键词： image processing

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181页 << < 88 89 90 91 92 93 94 95 96 97 > >>

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：