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

1,444 篇 会议
30 篇 期刊文献
16 册 图书

馆藏范围

1,490 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,167 篇 工学
- 933 篇 计算机科学与技术...
- 396 篇 软件工程
- 164 篇 电气工程
- 137 篇 光学工程
- 121 篇 信息与通信工程
- 96 篇 生物医学工程（可授...
- 91 篇 控制科学与工程
- 90 篇 生物工程
- 70 篇 机械工程
- 69 篇 仪器科学与技术
- 44 篇 电子科学与技术（可...
- 35 篇 化学工程与技术
- 32 篇 材料科学与工程（可...
- 15 篇 土木工程
- 14 篇 力学（可授工学、理...
- 13 篇 建筑学
394 篇 理学
- 220 篇 数学
- 181 篇 物理学
- 103 篇 生物学
- 74 篇 统计学（可授理学、...
- 37 篇 化学
- 13 篇 系统科学
330 篇 医学
- 323 篇 临床医学
- 33 篇 基础医学(可授医学...
- 25 篇 药学(可授医学、理...
107 篇 管理学
- 69 篇 图书情报与档案管...
- 42 篇 管理科学与工程(可...
37 篇 艺术学
- 37 篇 设计学（可授艺术学...
15 篇 农学
- 12 篇 作物学
10 篇 法学
- 10 篇 社会学
5 篇 教育学
3 篇 经济学
2 篇 军事学

主题

142 篇 pattern recognit...
110 篇 image segmentati...
94 篇 feature extracti...
83 篇 image processing
63 篇 image analysis
56 篇 image color anal...
41 篇 principal compon...
40 篇 face recognition
38 篇 image recognitio...
31 篇 support vector m...
30 篇 deep learning
30 篇 local binary pat...
30 篇 image reconstruc...
29 篇 computer vision
29 篇 shape
27 篇 image classifica...
26 篇 image enhancemen...
26 篇 pattern matching
25 篇 training
24 篇 neural networks

机构

7 篇 institute of aut...
5 篇 inesc tec porto
5 篇 univ porto fac e...
4 篇 guangdong univer...
4 篇 univ porto fac c...
4 篇 south china univ...
4 篇 microsoft resear...
4 篇 king fahd univer...
4 篇 shanghai jiao to...
3 篇 córdoba
3 篇 comp vis ctr bar...
3 篇 department of co...
3 篇 tarbiat modares ...
3 篇 univ porto fac e...
3 篇 inst res fundame...
3 篇 chitkara univers...
3 篇 universidade de ...
3 篇 harvard univ sch...
3 篇 ryerson univ dep...
3 篇 univ alicante ui...

作者

11 篇 cardoso jaime s.
9 篇 vidal enrique
8 篇 calvo-zaragoza j...
8 篇 oliveira helder ...
6 篇 lee seong-whan
6 篇 suen c.y.
5 篇 casacuberta fran...
5 篇 marques jorge s.
5 篇 pina pedro
4 篇 shengli xie
4 篇 pietikainen matt...
4 篇 bioucas-dias jos...
4 篇 dongbin zhao
4 篇 c.y. suen
4 篇 forero manuel g.
4 篇 yuanqing li
4 篇 el-sayed m. el-a...
4 篇 derong liu
4 篇 weickert joachim
4 篇 singh s

语言

1,454 篇 英文
33 篇 其他
6 篇 中文
1 篇 日文

检索条件"任意字段=3rd Iberian Conference on Pattern Recognition and Image Analysis"

共 1490 条记录，以下是341-350 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：

Novel Block Diagonalization for Reducing Features and Computations in Medical Diagnosis 33rd

Novel Block Diagonalization for Reducing Features and Comput...

引用

33rd Australasian Joint conference on Artificial Intelligence (AI)

作者： Ghani, Tahira Oommen, B. John Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9783030649838;9783030649845

Diagonalization is an "age old" technique from Linear Algebra, and it has had significant applications in pattern recognition (PR) and data pre-processing. By using the eigenvectors of the covariance matrix of a single class as the basis vectors describing the feature space, the transformed data can be rendered to have a diagonal covariance matrix. If the covariance matrices of two classes are utilized, the covariance matrix of transformed data of the first class can be the made the Identity, while that of the second can be diagonal, implying independence in the case of Normally distributed data (Similar diagonalizing schemes form the basis of the Principal Component analysis (PCA) and some feature selection/reduction etc. schemes.). In all of the cases reported in the literature, the entire covariance matrix is diagonalized, which is, computationally, a very tedious and cumbersome process. In this paper, we propose a radically different paradigm where we opt to render the transformed data to be block diagonalized. In other words, the covariance of the transformed data is made up of a predetermined number of block matrices, implying that these corresponding features are assumed to be correlated, while the others are assumed independent. Regression is now done by getting the best value based on each of these sub-blocks and averaging between them. This is essentially an ensemble machine, where the sub-blocks lead to their own respective regression values, which are then averaged to obtain the overall solution. This technique has been used to analyze the survival rate of cancer patients depends on the type of cancer, the treatments that the patient has undergone, and the severity of the cancer when the treatment was initiated. In our prima facie study, we consider adenocarcinoma, a type of lung cancer detected in chest Computed Tomography (CT) scans on the entire lung, and images that are "sliced" versions of the scans as one progresses along the thoracic region. The r

关键词： Diagonalization Block diagonalization Medical image processing Lung cancer treatment Prediction of survival rates

来源：评论

学校读者我要写书评

暂无评论

Probability κ-means Clustering for Neural Network Architecture 19

Probability κ-means Clustering for Neural Network Architect...

引用

Proceedings of the 3rd International conference on Advances in Artificial Intelligence

作者： Ka-Hou Chan Wei Ke Sio-Kei Im School of Applied Sciences Macao Polytechnic Institute Macao China Macao Polytechnic Institute Macao China

ISBN: (纸本)9781450372534

Cluster analysis indices are aimed at classifying the elements used to estimate the quality of the categories based on their similarity. It is a challenging task because, with the same data set, there may be many partitions that fit natural groupings of a given data set. Applications of clustering can include pattern recognition, image analysis and information retrieval. We propose an approach based on the κ-means concept that clustering centers more often have a higher density than their neighbors: then we use a probability κ-means algorithm to achieve fuzzy clustering in continuous form over a relatively large distance from other points with higher densities. Further, in order to follow the mainstream neural network architecture, we define a favorable activation function and corresponding loss function for the clustering iteration. Our method come from the basis of a clustering procedure in which the number of clusters arises intuitively and clusters are achieved regardless of the high dimensions. We demonstrate the result of complete algorithm on several clustering test cases.

关键词： Neural Network Architecture Clustering analysis probability κ-means

来源：评论

学校读者我要写书评

暂无评论

Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps 32

Condensation-Net: Memory-Efficient Network Architecture with...

引用

32nd IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Chen, Tse-Wei Yoshinaga, Motoki Gao, Hongxing Tao, Wei Wen, Dongchao Liu, Junjie Osa, Kinya Kato, Masami Canon Inc Device Technol Dev Headquarters Ohta Ku 30-2Shimomaruko 3 Chome Tokyo 1468501 Japan Canon Informat Technol Beijing Co Ltd 12A FloorYingu Bldg9 Beisihuanxi Rd Beijing Peoples R China

ISBN: (纸本)9781728125060

"Lightweight convolutional neural networks" is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware platform, it is necessary to reduce the memory size and the computational cost. The contribution of this paper is stated as follows. First, we propose an algorithm to process a specific network architecture (Condensation-Net) without increasing the maximum memory storage for feature maps. The architecture for virtual feature maps saves 26.5% of memory bandwidth by calculating the results of cross-channel pooling before storing the feature map into the memory. Second, we show that cross-channel pooling can improve the accuracy of object detection tasks, such as face detection, because it increases the number offilter weights. Compared with Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and 1.5% for full-precision networks when the false-positive rate is 0.1. Last but not the least, the analysis results show that the overhead to support the cross-channel pooling with the proposed hardware architecture is negligible small. The extra memory cost to support Condensation-Net is 0.2% of the total size, and the extra gate count is only 1.0% of the total size.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

K-mean and HSV model based segmentation of unhealthy plant leaves and classification using machine learning approach

K-mean and HSV model based segmentation of unhealthy plant l...

引用

3rd Smart Cities Symposium (SCS 2020)

作者： V. K. Trivedi P. K. Shukla P. K. Dutta Lakshmi Narain College of Technology Department of Computer Science & Engineering Bhopal India Amity University Kolkata Department of Engineering Amity School of Engineering and Technology Kolkata India

To support agriculture, disease detection and classification from crop plant become important aspect today. Manually disease detection requires enormous amount of work, and also requires the too much processing time, therefore image processing play important role for the detection of crop plant diseases. The proposed method automatic segment and classify different leaf disease regions from the plant images. The proposed method works on three steps: In the first step, pre-processing is applied on the plant image that be made of median filter and contrast stretching in order to remove the digitization noise and enhance the contrast of the image, respectively. In the second step, K-means clustering algorithm is employed on L*a*b* color model of leaf image that segments pixels into uniform sets. These segments then are converted into HSV color model and the segment that has maximum hue value among other segments are extracted as a disease segment. Finally, feature are extracted from disease segment and apply machine learning i.e. Multi SVM classifier to classify crop image into various class based on various diseases on crop images. Proposed technique is implemented on Matlab *** conduct the experiments on different plant image we have used various crop disease images from various source. Accuracy of our proposed system is approx. 86.67%. Keywords:

关键词： plant diseases crops support vector machines image colour analysis pattern clustering learning (artificial intelligence) median filters feature extraction agriculture image classification image segmentation image denoising image filtering image enhancement

来源：评论

学校读者我要写书评

暂无评论

Seismic image pattern analysis Using Fuzzy Logic Controller

Seismic Image Pattern Analysis Using Fuzzy Logic Controller

引用

International conference on Intelligent Sustainable Systems (ICISS)

作者： S. Jayachitra M. Arun Kumar G. Aravindh R. Sangeetha P. Sasikala Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Seismic wave is the wave of energy that travels via the earth as a consequence of an earthquake or a volcano that occurs with low-frequency acoustic energy. By analyzing the Seismic earthquake patterns it is possible to find whether the earthquake will occur or not. In this project, the seismic data are analyzed by a Fuzzy Logic Controller. But the seismic data are affected by some unwanted artifacts. The de-noising method has the capacity to reduce the artifacts. Here the Contourlet transforms scheme gives better noise suppression in seismic images and the signal to noise ratio is improved. Further, the de-noised seismic image is segmented using Wavelet transforms for feature extraction. The extracted features can be analyzed by fuzzy Logic Controller which shows whether the seismic image is normal or abnormal. This analysis gives better results in the earthquake analysis and also in the construction of sensitive power plants.

关键词： Feature extraction image segmentation Fuzzy logic Wavelet transforms Noise reduction Earthquakes conferences

来源：评论

学校读者我要写书评

暂无评论

Classification and Change Detection Using Multi-periodic Harmonic analysis 3

Classification and Change Detection Using Multi-periodic Har...

引用

3rd International conference on Vision, image and Signal Processing (ICVISP)

作者： Jun, Myunghee Lee, Sang-Hoon Software Engn Anyang Univ Kyunggi Do South Korea Gachon Univ Ind & Business Engn Kyunggi Do South Korea

ISBN: (纸本)9781450376259

Time-series of satellite images have been used to identify and monitor land cover change. Long-term datasets are very useful to examine an area over a period and see what changes have occurred. It is not an easy task to develop satisfactory change detection algorithms due to the processing complexity and extraction of meaningful change pattern of interest. In an effort to find an appropriate approach for this challenge, this paper presents a harmonic model-based change detection method using time- series of satellite images. The proposed algorithm is based on the temporal profile over time for the long-term change rather than a temporary change. A harmonic model can characterize the temporal variability of land covers whose signatures exhibit seasonal trends since components of the harmonic function inherently contain temporal information about seasonal changes. Several experiments were conducted on a multi-temporal dataset of Moderate Resolution Imaging Spectroradiometer (MODIS) over the Korean peninsula, in the time interval of 2012-2016. The results indicate that the proposed algorithm has a great potential for monitoring land cover condition and annual long-term land-cover change over large regions.

关键词： Land Cover Classification Change Detection Harmonic Model MODIS

来源：评论

学校读者我要写书评

暂无评论

Radiology reports automated annotation performance: rule-based machine learning vs deep learning

Radiology reports automated annotation performance: rule-bas...

引用

3rd Smart Cities Symposium (SCS 2020)

作者： A. Sahl S. Hasan King Khalid University Computer Science Dept. Guraiger Abha 62529 Saudi Arabia Universiti Teknologi Malaysia Sultan Ibrahim Chancellery Building Jalan Iman 81310 Skudai Johor Malaysia

Currently the availability of medical images data itself is not a challenge, but the acquisition of relevant annotations/labelling for these images is. An unsolved challenge in medical image analysis and its related radiology reports is to turn the radiology reports into an accurate annotations or structured labels in an automated manner using Natural Language Processing (NLP). Traditional rule-based Machine Learning (ML) and Deep Learning (DL) algorithms are two approaches which were used in the extraction and annotation of radiology text reports. This paper provides a comparison of the performance of traditional rule-based machine learning to that of using deep learning algorithms in the extraction and annotation of radiology text reports using natural language processing (NLP). It presents an accurate analysis of the two discussed approaches and supports such with critical evidence related to the effects both approaches have on the issue of radiological reporting. According to the evaluation of the obtained data, the research concludes that Deep Learning (DL) models perform with higher efficiency than both the TML and validated NLP classifiers in terms of accuracy in extracting the required text information from the free-text radiology reports.

关键词： medical information systems text analysis radiology natural language processing deep learning (artificial intelligence) pattern classification

来源：评论

学校读者我要写书评

暂无评论

Natural image Character recognition Using One Class-Principal Component analysis Classifier 3

Natural Image Character Recognition Using One Class-Principa...

引用

3rd International conference on pattern analysis and Intelligent Systems (PAIS)

作者： Zouaoui, Fatima Chibani, Youcef USTHB Fac Elect & Comp Sci Lab Ingn Syst Intelligents & Commun 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9781538642382

The character recognition from natural scene image is used in many applications as for instance in intelligent transportation systems. Indeed, many recent methods have been focused to design a better feature representation of the character. However, the performances of such system is still far from perfect since it depends also of the appropriate choice of the classifier. Hence, the present paper addresses the problem of natural character recognition and tries to investigate the performance of the One Class-Principal Component analysis Classifier (OC-PCA). The OC-PCA classifier has the main advantage to absorb the high dimension of the feature vector with few samples. For evaluating the performance of the proposed classifier, experimental results are conducted on challenging Char74k dataset highlighting its robustness against the state-of-art.

关键词： Character recognition Natural scene images Chars74k dataset oBIF OC-PCA

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal land use analysis and urban growth dynamic prediction in the kingdom of Bahrain

Spatio-temporal land use analysis and urban growth dynamic p...

引用

3rd Smart Cities Symposium (SCS 2020)

作者： E.A. Alburshaid M.A. Mangoud National Space Science Agency Bahrain College of Engineering University of Bahrain Bahrain

This paper aims to classify and predict land use/cover (LULC) for coarse spatial resolution satellite imageries of the Northern Governorate in the Kingdom of Bahrain during the period from 2016 to 2050. Satellite images from the European Space Agency portal have been used to observe, quantify, and classify them into four classes which are water, urban area, barren land, and agricultural area. Three classifications methods namely, Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) have been conducted to classify these imageries and the results are compared based on the accuracy assessment matrices. Land change modeller (LCM) model has been used to predict the land use of the Northern Governorate based on historical transition trend and the pattern changes. This prediction model is constructed based on the Multilayer Perceptron Neural Network (MLPNN) and Markov Chain (MC) to predict the spatial dynamics of urban growth in 2030 and 2050. The prediction results show how the urban area is going to expand significantly from during (2016-2050). Also, the paper presents the future status of the agricultural area, water and barren land.

关键词： multilayer perceptrons image classification remote sensing agriculture land use support vector machines terrain mapping neural nets land cover land use planning geophysical image processing Markov processes

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analytic Scheme for Classification of Novel Models

A Comprehensive Analytic Scheme for Classification of Novel ...

引用

International conference on Intelligent Sustainable Systems (ICISS)

作者： D. Dakshayani Himabindu S. Praveen Kumar VNR VJIET Hyderabad T.S India GIT GITAM Visakhapatnam Andhra pradesh

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Classification is one of the major tasks in supervised learning. This had a great impact on deep leaning in developing various State-of-the-art architectures every year. These models can capture invariances by extracting the finest features by variant design models developed using Convolution Neural Networks. These deep networks have proven their resilience in the large scale image recognition by overhauling the existing drawbacks in *** this research, a complete analytical scheme is developed to extract features from various models and to depict its performance on Fruits-360 dataset. During the analysis, the data is processed from feature extraction block to classification block. After the classification, the individual models' performance is evaluated and assessed with standard classification metrics. Out of these, an optimal model is selected in all prospects. It is observed that VGG models tend to outperform the existing models by capturing invariances by providing transferability on variant tasks. The networks VGG-16, VGG-19 obtained the highest precision of 97.2±0.005, 96.6±0.66 respectively. A pair-plot is obtained to analyse the behaviour pattern of predicted labels to that of true labels. This research is a first attempt to analyse the behaviour of pre-trained models on Fruits-360 dataset.

关键词： Feature extraction Task analysis Computer architecture Training Convolution Analytical models Error analysis

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149页 << < 31 32 33 34 35 36 37 38 39 40 > >>

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：