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检索条件"任意字段=7th International Workshop on Graph-Based Representations in Pattern Recognition"

共 197 条记录，以下是71-80 订阅

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Proceedings of the 7th international workshop on pattern recognition in Information Systems PRIS 2007;in Conjunction with ICEIS 2007

Proceedings of the 7th International Workshop on Pattern Rec...

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7th international workshop on pattern recognition in Information Systems PRIS 2007;In Conjunction with ICEIS 2007

ISBN: (纸本)9789728865931

the proceedings contain 24 papers. the topics discussed include: complex statistical models for object recognition and mass spectrometry;semi-supervised and active learning;reliable biometrical analysis in biodiversity information systems;string patterns: from single clustering to ensemble methods and validation;a novel distance measure for interval data;bridging the gap between Naive Bayes and maximum entropy text classification;a weight vector feature for 3D shape matching;extending morphological signatures for visual pattern recognition;a contribution to ancient cadastral maps interpretation through colour analysis;texture learning by fractal compression;incremental non-negative matrix factorization for dynamic background modelling;and improving isometric hand gesture identification for HCI based on independent component analysis in bio-signal processing.

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A Global Method for Reducing Multidimensional Size graphs

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8th IAPR-TC-15 international workshop on graph-based representations in pattern recognition (GbRPR)

作者： Cerri, Andrea Frosini, Patrizio Kropatsch, Walter G. Landi, Claudia Vienna Univ Technol Fac Informat Pattern Recognit & Image Proc Grp Vienna Austria Univ Bologna ARCES Dipartimento Matemat I-40126 Bologna Italy Univ Modena & Reggio Emilia Dipartimento Sci & Metodi Ingn Modena Italy

ISBN: (纸本)9783642208447

this paper introduces the concept of discrete multidimensional size function, a mathematical tool studying the so-called size graphs. these graphs constitutes an ingredient of Size theory, a geometrical/topological approach to shape analysis and comparison. A global method for reducing size graphs is presented, together with a theorem stating that size graphs reduced in such a way preserve all the information in terms of multidimensional size functions. this approach can lead to simplify the effective computation of discrete multidimensional size functions, as shown by examples.

关键词： Functions

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graph-based representations in pattern recognition 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Pasquale Foggia Mario Vento Cheng-Lin Liu

ISBN: (数字)9783319589619

ISBN: (纸本)9783319589602

this book constitutes the refereed proceedings of the 11th IAPR-TC-15 international workshop on graph-based Representation in pattern recognition, GbRPR 2017, held in Anacapri, Italy, in May 2017. the 25 full papers and 2 abstracts of invited papers presented in this volume were carefully reviewed and selected from 31 submissions. the papers discuss research results and applications in the intersection of pattern recognition, image analysis, graph theory, and also the application of graphs to pattern recognition problems in other fields like computational topology, graphic recognition systems and bioinformatics.

关键词： pattern recognition Image Processing and Computer Vision Computer graphics Discrete Mathematics in Computer Science Data Structures Algorithm Analysis and Problem Complexity

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Reeb graphs through local binary patterns 10

Reeb graphs through local binary patterns

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

this paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a shape. the nodes in the graph represent critical points (positions of change in the topology), while edges represent topological persistence. We present an approach to compute such critical points using Local Binary patterns. For one pixel the Local Binary pattern feature vector is derived comparing this pixel to its neighbouring pixels in an environment of a certain radius. We start with an initial segmentation and a medial axis representation. Along this axis critical points are computed using Local Binary patterns with the radius, defining the neighbouring pixels, set a bit larger than the radius according to the medial axis transformation. Critical points obtained in this way form the node set in a Reeb graph, edges are given through the connectivity of the skeleton. this approach aims at improving the representation of flawed segmented data. In the same way segmentation artefacts, as for example single pixels representing noise, may be corrected based on this analysis. © Springer international Publishing Switzerland 2015.

关键词： Image segmentation

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On the Evaluation of Tensor-based representations for Optimum-Path Forest Classification 7th

On the Evaluation of Tensor-Based Representations for Optimu...

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7th IAPR TC3 workshop on Artificial Neural Networks in pattern recognition (ANNPR)

作者： Lopes, Ricardo Costa, Kelton Papa, Joao Inst Pesquisas Eldorado Campinas SP Brazil Sao Paulo State Univ Dept Comp Sao Paulo Brazil

ISBN: (纸本)9783319461823;9783319461816

Tensor-based representations have been widely pursued in the last years due to the increasing number of high-dimensional datasets, which might be better described by the multilinear algebra. In this paper, we introduced a recent pattern recognition technique called Optimum-Path Forest (OPF) in the context of tensor-oriented applications, as well as we evaluated its robustness to space transformations using Multilinear Principal Component Analysis in both face and human action recognition tasks considering image and video datasets. We have shown OPF can obtain more accurate recognition rates in some situations when working on tensor-oriented feature spaces.

关键词： Optimum-Path Forest Tensors Gait and face recognition

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Identifying Instances of Model Design patterns and Antipatterns Using Model Clone Detection 7

Identifying Instances of Model Design Patterns and Antipatte...

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2015 IEEE ACM 7th international workshop on Modeling in Software Engineering

作者： Stephan, Matthew Cordy, James R. Miami Univ Dept Comp Sci & Software Engn Oxford OH 45056 USA Queens Univ Sch Comp Kingston ON Canada

ISBN: (纸本)9781467370561

A hurdle in the growth of model driven software engineering is our ability to evaluate the quality of models automatically. One perspective is that software quality is a function of the existence, or lack thereof, of good and bad properties, also known as patterns and antipatterns, respectively. In this paper, we introduce the notion of using model clone detection to detect model pattern and antipattern instances by looking for models that are cross clones of pattern models. By detecting patterns at the model level, analysis is accomplished earlier in the engineering process, can be applied to primarily model-based projects, and remains at the same level of abstraction that engineers are used to. We outline the process of using model clone detection for this purpose, including representing the patterns and detection of instances. We present some Simulink examples of pattern representations and discuss future work and research in the area.

关键词： Design patterns Model Clone Detection Model Comparison Model Quality antipatterns design pattern level of abstraction Clone Cells model design software quality Software engineering

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Leveraging Knowledge graph Embeddings to Enhance Contextual representations for Relation Extraction 17th

Leveraging Knowledge Graph Embeddings to Enhance Contextual ...

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17th international Conference on Document Analysis and recognition workshop (ICDAR)

作者： Laleye, Frejus A. A. Rakotoson, Loic Massip, Sylvain Opscidia Paris France

ISBN: (纸本)9783031415005;9783031415012

Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task;however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. Our objective is to showcase that by leveraging the hierarchical structure and relational distribution of entities within a corpus without introducing external knowledge, a relation extraction model can achieve significantly enhanced performance. We therefore proposed a relation extraction approach based on the incorporation of pretrained knowledge graph embeddings at the corpus scale into the sentence-level contextual representation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach. the obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.

关键词： Relation extraction Knowledge graph Embeddings Contextual representation

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Find More Accurate Text Boundary for Scene Text Detection 26

Find More Accurate Text Boundary for Scene Text Detection

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26th international Conference on pattern recognition / 8th international workshop on Image Mining - theory and Applications (IMTA)

作者： Pan, Runqiu Li, Zezhou Zhu, Anna Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Arbitrary shape text detection is still an open problem due to several challenges, e.g., dense text adhesion, various sizes, and noises. In this work, we propose a novel scene text detection network by extracting and connecting a set of points on the boundary of each text instance. Our method mainly consists of three branches, including text center line (TCL) prediction, text orientation (TO) prediction, and text boundary offset (TBO) prediction to get the text boundary point proposals. Utilize these proposals, and the final refinement results can be obtained by point sampling and graph attention network(GAT). the detector can overcome the text instance sticking problem with these text boundary representations. Additionally, we propose distance-based dice loss and instance-aware L1 loss to remove false positives and get over various text sizes on text boundary offset prediction respectively. In this way, our method can directly and efficiently generate accurate text boundaries without any post-processing. Extensive experiments on publicly available datasets show the effectiveness of our design and training strategy, which also demonstrates our method's state-of-the-art performance for arbitrary shape text detection.

关键词： Training Shape Adhesives Detectors pattern recognition Proposals

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graph-based representations in pattern recognition 2005

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丛书名： Lecture Notes in Computer Science

2005年

作者： Luc Brun Mario Vento

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Report on the first contest on graph matching algorithms for pattern search in biological databases 10

Report on the first contest on graph matching algorithms for...

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

作者： Carletti, Vincenzo Foggia, Pasquale Vento, Mario Jiang, Xiaoyi Department of Information Engineering Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy Department of Computer Science University of Münster Münster Germany

ISBN: (纸本)9783319182230

graphs are a powerful data structure that can be applied to several problems in bioinformatics, and efficient graph matching is often a tool required for several applications that try to extract useful information from large databases of graphs. While graph matching is in general a NP-complete problem, several algorithms exist that can be fast enough on practical graphs. However, there is no single algorithm that is able to outperform the others on every kind of graphs, and so it is of paramount importance to assess the algorithms on graphs coming from the actual problem domain. To this aim, we have organized the first edition of the Contest on graph Matching Algorithms for pattern Search in Biological Databases, hosted by the ICPR2014 Conference, so as to provide an opportunity for comparing state-of-the-art matching algorithms on a new graph database built using several kinds of real-world graphs found in bioinformatics applications. the participating algorithms were evaluated with respect to both their computation time and their memory usage. this paper will describe the contest task and databases, will provide a brief outline of the participating algorithms, and will present the results of the contest. © Springer international Publishing Switzerland 2015.

关键词： Bioinformatics

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