检索结果-内蒙古大学图书馆

IEEE International Symposium on Intelligent Signal Processing

作者： Vega, J. Ratta, G. Murari, A. Castro, P. Dormido-Canto, S. Dormido, R. Farias, G. Pereira, A. Portas, A. de la Luna, E. Pastor, I. Sanchez, J. Duro, N. Castro, R. Santos, M. Vargas, H. EURATOM CIEMAT Fus Avda Complutense 22 Madrid 28040 Spain Consorzio RFX-Associazione I-35127 Padua Italy Univ Nacl Educ Dist Dept Inform Automat Madrid Spain Univ Complutense Madrid Dept Arquitectura Computadores Autom E-28040 Madrid Spain

ISBN: (纸本)9781424408290

Physics studies in fusion devices require statistical analyses of a large number of discharges. Given the complexity of the plasma and the non-linear interactions between the relevant parameters, connecting a physical phenomenon with the signal patterns that it generates can be quite demanding. Up to now, data retrieval has been typically accomplished by means of signal name and shot number. The search of the temporal segment to analyze has been carried out in a manual way. Manual searches in databases must be replaced by intelligent techniques to look for data in an automated way. structural pattern recognition techniques have proven to be very efficient methods to index and retrieve data in JET and TJ-II databases. Waveforms and images can be accessed through several structural pattern recognition applications.

关键词： data mining information retrieval image processing structural pattern recognition

来源：评论

学校读者我要写书评

暂无评论

MAKING structural pattern recognition TRACTABLE BY LOCAL INHIBITION

MAKING STRUCTURAL PATTERN RECOGNITION TRACTABLE BY LOCAL INH...

引用

4th International Conference on Computer Vision Theory and Applications

作者： Michaelsen, E. Doktorski, L. Arens, M. Forschungsinst Optron & Mustererkennung D-76275 Ettlingen Germany

ISBN: (纸本)9789898111692

Declarative knowledge and control decisions on the sequence of interpretation acts are separated in a structural pattern recognition system. The control can be optimized leaving the knowledge fixed. A simple production system is used as declarative example knowledge. It is tailored to recognize and locate rectangles in images - where object primitives are several thousand very short contour segments. Different control strategies can be realized: (i) a simple quality driven bottom-up control;(ii) an heuristic strategy punishing object instances which have been partner in an already performed reduction and (iii) a new psychologically inspired strategy that combines local inhibition with less local excitation. These strategies are compared quantitatively on synthetic data and qualitatively on a real aerial image.

关键词： structural pattern recognition Local inhibition

来源：评论

学校读者我要写书评

暂无评论

Graph Reduction Neural Networks for structural pattern recognition

Graph Reduction Neural Networks for Structural Pattern Recog...

引用

joint IAPR International Workshop on structural and Syntactic pattern recognition (SSPR) / Statistical Techniques in pattern recognition (SPR)

作者： Gillioz, Anthony Riesen, Kaspar Univ Bern Inst Comp Sci Bern Switzerland Univ Appl Sci Northwestern Switzerland Inst Informat Syst Olten Switzerland

ISBN: (纸本)9783031230271;9783031230288

In industry, business, and science large amounts of data are produced and collected. In some of these data applications, the underlying entities are inherently complex, making graphs the representation formalism of choice. Actually, graphs endow us with both representational power and flexibility. On the other hand, methods for graph-based data typically have high algorithmic complexity hampering their application in domains that comprise large entities. This motivates to research graph-based algorithms that are both efficient and reliable. In the present paper, we introduce a novel pattern recognition framework that consists of two basic parts. First, we substantially reduce the size of the original graphs by means of a graph neural network. Eventually, we use the reduced graphs in conjunction with graph edit distance and a distance-based classifier. On five datasets we empirically confirm the benefit of the novel reduction scheme regarding both classification performance and computation time.

关键词： structural pattern recognition Graph matching Graph reduction Graph neural network

来源：评论

学校读者我要写书评

暂无评论

On the separability of urban land-use categories in fine spatial scale land-cover data using structural pattern recognition

引用

ENVIRONMENT AND PLANNING B-PLANNING & DESIGN 2004年第3期31卷 397-418页

作者： Barr, SL Barnsley, MJ Steel, A Univ Leeds Sch Geog Leeds LS2 9JT W Yorkshire England Univ Coll Swansea Dept Geog Swansea SA2 8PP W Glam Wales

It has been widely asserted that the morphology of urban areas is a result of the interactions of urban function and urban form. This has led a number of studies to postulate, either explicitly or implicitly, that a mapping exists between the physical form (land cover) of the urban fabric and its corresponding function and activity (land use), although relatively little quantitative evidence has been presented to support this assertion. This paper presents the results of an investigation into the relationship between urban form and urban function using fine spatial scale digital map data. These are used to derive quantitative information on the morphological properties and spatial structure of the buildings present in a series of urban land-use categories identified in two urban areas (Cardiff and Orpington) in the United Kingdom. A statistical separability analysis of these land-use samples suggests that a mapping exists between urban form and function, which, if replicated for other urban areas, would allow urban land use to be inferred from an analysis of the spatial disposition of land-cover parcels, particularly buildings.

关键词： spatial scale structural pattern recognition urban area urban forms CONSTRUCTION IN SPACE Buildings

来源：评论

学校读者我要写书评

暂无评论

Evaluating structural pattern recognition for Handwritten Math via Primitive Label Graphs

Evaluating Structural Pattern Recognition for Handwritten Ma...

引用

20th Conference on Document recognition and Retrieval (DRR) held as part of the IS and T/SPIE Symposium on Electronic Imaging

作者： Zanibbi, Richard Mouchere, Harold Viard-Gaudin, Christian Rochester Inst Technol Dept Comp Sci 1 Lomb Mem Dr Rochester NY 14623 USA Univ Nantes LUNAM IRCCyN F-44306 Nantes France

ISBN: (纸本)9780819494313

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e. g. symbols) are frequently comprised of multiple primitives (e. g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

关键词： Evaluation Performance Metrics structural pattern recognition Math recognition Graphics recognition

来源：评论

学校读者我要写书评

暂无评论

Graphs in Computer Vision then and now: how Deep Learning has reinvigorated structural pattern recognition 22

Graphs in Computer Vision then and now: how Deep Learning ha...

引用

31st ACM Web Conference (WWW)

作者： Conte, Donatello Univ Tours Lab Informat Fondamentale & Appl Tours LIFAT EA 6 Tours France

ISBN: (纸本)9781450391306

Computer Vision Problems, such as object detection, object tracking, action recognition and so on, have been, in the past, usually addressed through Statistical pattern recognition techniques. SVM, Regression or Neural Networks, are some examples of classical statistical techniques that have been used, quite effectively, in many application contexts of computer vision. Nevertheless, some attempts have been proposed using more complex data structures (notably graphs) for solving Computer Vision Tasks. However, in terms of performances, their use did not have the same success as techniques based on vector representations. First part of this talk will present some of these proposals, in the context of object tracking ([1]), people re-identification ([3]) and action recognition ([2]). An graph representation is proposed in [1] to deal with occlusion problem. The representation is based on a graph pyramid, namely, each moving region is represented at different levels of resolution using a graph for each level. The algorithm compares the topmost levels of each pyramid in the association phase between moving objects in two consecutive frames. If the comparison outcome is sufficient to assign a label to each node the tracking algorithm stops. Instead, if some ambiguities arise (as it is the case when two objects over- lap), the algorithm is repeated using the next levels of the pyramids, until either a consistent labelling is found. The purpose of re-identification (reid) is to identify people coming back into the field of view of a camera or to recognize an individual through different cameras in a distributed network. At the heart of the process there is a comparison between signatures given probe and gallery sets. In [3] graphs are used to represent people appearance and comparison is done by means of Graph Kernels. Finally, action recognition is a classification problem in which each video representing an action has to be classified with the correct action label. In [2]

关键词： Computer Vision structural pattern recognition Graph Neural Networks

来源：评论

学校读者我要写书评

暂无评论

The state inspection of upper supporting roller of conveyor in service with structural pattern recognition and cluster norm algorithm of failure

The state inspection of upper supporting roller of conveyor ...

引用

作者： Ehui Huang Chunyang Gu Shanping Gao Jinling Song Shuting Li Junqin Zhou Zhenjie Chen Lizhi Gu Key Laboratory of Virtual Manufacturing Technology of Fujian Universities Quanzhou University of Information Engineering China University of Nottingham Quanzhou Normal University College of Mechanical Engineering and Automation Huaqiao University

In thermal power generation,coal transportation is an important link in the production process,and coal transportation by corridor conveyor is a common technical *** order to ensure the normal and continuous supply of coal,it is necessary to inspect the corridor facilities,especially the working status of conveyor rollers,and timely find and deal with abnormal *** is difficult to meet the requirements of modern thermal coal transportation inspection due to the heavy workload and high labor intensity.A kind of inspection robot instead of manual is proposed and *** robot system by CCD detection,convention scanning way,classifier design and abnormal structure pattern recognition algorithm,the displacement of conveyor roller state,center,loss of revolution,abstraction of severe abrasion and crack,forms the state vector,through clustering algorithm of norm,judgment is done for determination of the roller on the working state.

关键词： State vector structural pattern recognition Machine vision Conveyor upper roller state Cluster norm algorithm

来源：评论

学校读者我要写书评

暂无评论

引用

pattern recognition LETTERS 2012年第7期33卷 826-832页

作者： Duin, Robert P. W. Pekalska, Elzbieta Delft Univ Technol Pattern Recognit Lab Delft Netherlands Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

Human experts constitute pattern classes of natural objects based on their observed appearance. Automatic systems for pattern recognition may be designed on a structural description derived from sensor observations. Alternatively, training sets of examples can be used in statistical learning procedures. They are most powerful for vectorial object representations. Unfortunately, structural descriptions do not match well with vectorial representations. Consequently it is difficult to combine the structural and statistical approaches to pattern recognition. structural descriptions may be used to compare objects. This leads to a set of pairwise dissimilarities from which vectors can be derived for the purpose of statistical learning. The resulting dissimilarity representation bridges thereby the structural and statistical approaches. The dissimilarity space is one of the possible spaces resulting from this representation. It is very general and easy to implement. This paper gives a historical review and discusses the properties of the dissimilarity space approaches illustrated by a set of examples on real world datasets. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Dissimilarity representation Representation set Dissimilarity space Vector space structural pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Towards the unification of structural and statistical pattern recognition

引用

pattern recognition LETTERS 2012年第7期33卷 811-825页

作者： Bunke, Horst Riesen, Kaspar Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland

The field of pattern recognition is usually subdivided into the statistical and the structural approach. structural pattern recognition allows one to use powerful and flexible representation formalisms but offers only a limited repertoire of algorithmic tools needed to solve classification and clustering problems. By contrast, the statistical approach is mathematically well founded and offers many tools, but provides a representation formalism that is limited in its power and flexibility. Hence, both subfields are complementary to each other. During the last three decades several efforts have been made towards bridging the gap between structural and statistical pattern recognition in order to profit from the benefits of each approach and eliminate the drawbacks. The present paper reviews some of these attempts made towards the unification of structural and statistical pattern recognition and analyzes the progress that has been achieved. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Statistical pattern recognition structural pattern recognition Graph classification Graph clustering Graph kernel Graph embedding

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Classification Model for Digital Pathology Using structural and Statistical pattern recognition

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 2013年第2期32卷 474-483页

作者： Ozdemir, Erdem Gunduz-Demir, Cigdem Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkey

Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and grading. In this paper, we introduce an effective hybrid model that employs both structural and statistical pattern recognition techniques to locate and characterize the biological structures in a tissue image for tissue quantification. To this end, this hybrid model defines an attributed graph for a tissue image and a set of query graphs as a reference to the normal biological structure. It then locates key regions that are most similar to a normal biological structure by searching the query graphs over the entire tissue graph. Unlike conventional approaches, this hybrid model quantifies the located key regions with two different types of features extracted using structural and statistical techniques. The first type includes embedding of graph edit distances to the query graphs whereas the second one comprises textural features of the key regions. Working with colon tissue images, our experiments demonstrate that the proposed hybrid model leads to higher classification accuracies, compared against the conventional approaches that use only statistical techniques for tissue quantification.

关键词： Automated cancer diagnosis cancer graph embedding histopathological image analysis inexact graph matching structural pattern recognition

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：