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检索条件"任意字段=5th International Workshop on Graph-Based Representations in Pattern Recognition"

共 194 条记录，以下是131-140 订阅

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5th international workshop on Multiple Classifier Systems, MCS 2004

5th International Workshop on Multiple Classifier Systems, M...

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5th international workshop on Multiple Classifier Systems, MCS 2004

ISBN: (纸本)3540221441

the proceedings contain 37 papers. the special focus in this conference is on Bagging and Boosting. the topics include: Classifier ensembles for changing environments;classification and function estimation;boosting for noisy data;bagging decision multi-trees;a new approach to incremental learning;recursive ECOC learning machines;exact bagging with k-nearest neighbour classifiers;a maximum entropy approach;combining one-class classifiers to classify missing data;combining kernel information for support vector classification;combining classifiers using dependency-based product approximation with bayes error rate;combining dissimilarity-based one-class classifiers;a modular system for the classification of time series data;a probabilistic model using information theoretic measures for cluster ensembles;classifier fusion using triangular norms;dynamic integration of regression models;dynamic classifier selection by adaptive k-nearest-neighbourhood rule;spectral measure for multi-class problems;the relationship between classifier factorisation and performance in stochastic vector quantisation;a method for designing cost-sensitive ECOC;building graph-based classifier ensembles by random node selection;a comparison of ensemble creation techniques;multiple classifiers system for reducing influences of atypical observations;sharing training patterns among multiple classifiers;first experiments on ensembles of radial basis functions;an empirical bias-variance analysis;building diverse classifier outputs to evaluate the behavior of combination methods;an empirical comparison of hierarchical vs. two-level approaches to multiclass problems;experiments on ensembles with missing and noisy data;induced decision fusion in automated sign language interpretation and ensembles of classifiers derived from multiple prototypes and their application to handwriting recognition.

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Comparisons of two approaches of pattern recognition for text detection 5

Comparisons of two approaches of pattern recognition for tex...

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5th Symposium for Young Scientists in Technology, Engineering and Mathematics, SYSTEM 2020

作者： Lis, Ewa Kluger, Roman Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

the paper describes two methods of pattern recognition, one based on soft sets, another based on neural networks. Soft set theory is quite recently developed method of AI. that approach has rather simple mathematical background, but can perform satisfactory. Widespread neural network approach demands much more calculating power to perform at the same level of accuracy. Some hints to make neural network perform better have been written. Authors explain necessary theoretical terms and definitions then describe their idea of two AI systems. At the end some results of first system are presented. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Character recognition

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Dataset of Pages from Early Printed Books with Multiple Font Groups 19

Dataset of Pages from Early Printed Books with Multiple Font...

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5th international workshop on Historical Document Imaging and Processing (HIP)

作者： Seuret, Mathias Limbach, Saskia Weichselbaumer, Nikolaus Maier, Andreas Christlein, Vincent Friedrich Alexander Univ Pattern Recognit Lab Erlangen Germany Gutenberg Inst Weltliteratur & Schriftorient Medi Abt Buchwissensch Mainz Germany

ISBN: (纸本)9781450376686

based on contemporary scripts, early printers developed a large variety of different fonts. While fonts may slightly differ from one printer to another, they can be divided into font groups, such as Textura, Antiqua, or Fraktur. the recognition of font groups is important for computer scientists to select adequate OCR models, and of high interest to humanities scholars studying early printed books and the history of fonts. In this paper, we introduce a new, public dataset for the recognition of font groups in early printed books, and evaluate several state-of-the-art CNNs for the font group recognition task. the dataset consists of more than 35 600 page images, each page showing up to five different font groups, of which ten are considered in this dataset.

关键词： uments document analysis incunabula book history type typography fonts Antiqua Italic Textura Rotunda Gotico-Antiqua Bastarda Schwabacher Fraktur Greek Hebrew

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Database Systems for Advanced Applications. DASFAA 2021 international workshops 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Christian S. Jensen Ee-Peng Lim De-Nian Yang Chia-Hui Chang Jianliang Xu Wen-Chih Peng Jen-Wei Huang Chih-Ya Shen

ISBN: (数字)9783030732165

ISBN: (纸本)9783030732158

the 29 revised full papers presented in this book were carefully reviewed and selected from 84 submissions.;6th international workshop on Big Data Quality Management (BDQM 2021);5th international workshop on graph Data Management and Analysis (GDMA 2021);First international workshop on Machine Learning and Deep Learning for Data Security Applications (MLDLDSA 2021);6th international workshop on Mobile Data Management, Mining, and Computing on Social Network (MobiSocial 2021);2021 international workshop on Mobile Ubiquitous Systems and Technologies (MUST 2021)

关键词： Data Mining and Knowledge Discovery Computer Communication Networks Data Structures and Information theory Artificial Intelligence Computer Imaging, Vision, pattern recognition and graphics Computer Systems Organization and Communication Networks

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Construction of combinatorial pyramids

Construction of combinatorial pyramids

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4th IAPR international workshop on graph based representations in pattern recognition, GbRPR 2003

作者： Brun, Luc Kropatsch, Walter G. Université de Reims France Institute for Computer-aided Automation Pattern Recognition and Image Processing Group Vienna Univ. of Technology Austria

ISBN: (纸本)354040452X

Irregular pyramids are made of a stack of successively reduced graphs embedded in the plane. Each vertex of a reduced graph corresponds to a connected set of vertices in the level below. One connected set of vertices reduced into a single vertex at the above level is called the reduction window of this vertex. In the same way, a connected set of vertices in the base level graph reduced to a single vertex at a given level is called the receptive field of this vertex. the graphs used in the pyramid may be region adjacency graphs, dual graphs or combinatorial maps. this last type of pyramids are called Combinatorial Pyramids. Compared to usual graph data structures, combinatorial maps encode one graph and its dual within a same formalism and offer an explicit encoding of the orientation of edges around vertices. this paper describes the construction scheme of a Combinatorial Pyramid. We also provide a constructive definition of the notions of reduction windows and receptive fields within the Combinatorial Pyramid framework. © Springer-Verlag Berlin Heidelberg 2003.

关键词： graphic methods

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Reducing the Computational Complexity of the Eccentricity Transform of a Tree 13th

Reducing the Computational Complexity of the Eccentricity...

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13th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2023

作者： Banaeyan, Majid Kropatsch, Walter G. Pattern Recognition and Image Processing Group TU Wien Vienna Austria

ISBN: (纸本)9783031427947

this paper proposes a novel approach to reduce the computational complexity of the eccentricity transform (ECC) for graph-based representation and analysis of shapes. the ECC assigns to each point within a shape its geodesic distance to the furthest point, providing essential information about the shape’s geometry, connectivity, and topology. Although the ECC has proven valuable in numerous applications, its computation using traditional methods involves heavy computational complexity. To overcome this limitation, we present a method that computes the ECC of a tree, significantly reducing the computational complexity from O(n2log(n) ) to O(b), where n and b are the numbers of vertices and branching points in the tree, respectively. Our method begins by computing the ECC for tree structures, which are simpler representations of shapes. Subsequently, we introduce the concept of a 3D curve that corresponds to a smooth shape without holes, enabling the computation of the ECC for more complex shapes. By leveraging the 3D curve representation, our method provides an upper-bound approximation of the ECC, which can be effectively utilized in various applications. the proposed approach not only preserves the valuable properties of the ECC but also significantly reduces the computational burden, making it a more efficient and practical solution for graph-based representation and analysis of shapes in both 2D and 3D contexts. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational complexity

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5th international workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th international Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

5th International Workshop on Predictive Intelligence in Med...

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5th international workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th international Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

ISBN: (纸本)9783031169182

the proceedings contain 19 papers. the special focus in this conference is on Predictive Intelligence in Medicine. the topics include: Discriminative Deep Neural Network for Predicting Knee OsteoArthritis in Early Stage;long-Term Cognitive Outcome Prediction in Stroke Patients Using Multi-task Learning on Imaging and Tabular Data;Quantifying the Predictive Uncertainty of Regression GNN Models Under Target Domain Shifts;investigating the Predictive Reproducibility of Federated graph Neural Networks Using Medical Datasets;learning Subject-Specific Functional Parcellations from Cortical Surface Measures;a Triplet Contrast Learning of Global and Local representations for Unannotated Medical Images;predicting Brain Multigraph Population from a Single graph Template for Boosting One-Shot Classification;Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores Using graph Neural Networks and Meta-learning;bridging the Gap Between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing;Multi-tracer PET Imaging Using Deep Learning: Applications in Patients with High-Grade Gliomas;multiple Instance Neuroimage Transformer;intervertebral Disc Labeling with Learning Shape Information, a Look once Approach;Mixup Augmentation Improves Age Prediction from T1-Weighted Brain MRI Scans;Diagnosing Knee Injuries from MRI with Transformer based Deep Learning;MISS-Net: Multi-view Contrastive Transformer Network for MCI Stages Prediction Using Brain18 F-FDG PET Imaging;transDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation;preface.

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Fuzzy Logic and Applications - 5th international workshop, WILF 2003, Revised Selected Papers

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5th international workshop on Fuzzy Logic and Applications, WILF 2003

ISBN: (纸本)3540310193

the proceedings contain 40 papers. the topics discussed include: rough-fuzzy granular computing, case based reasoning and data mining;some complexity results on fuzzy description logics;an evolutionary approach to ontology-based user model acquisition;mathematical modeling of passage dynamic function;statistical analysis of the different operator involved in fuzzy inference process;fuzzy relational neural networks for data analysis;a neuro-fuzzy system for the prediction of the vehicle traffic flow;an iterative algorithm for fuzzy total cost in an inventory without backorder case;and fuzzy spatial relationship for model-based pattern recognition in images and spatial reasoning under imprecision.

关键词： Fuzzy sets

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7th IAPR TC3 workshop on Artificial Neural Networks in pattern recognition, ANNPR 2016

7th IAPR TC3 Workshop on Artificial Neural Networks in Patte...

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7th IAPR TC3 workshop on Artificial Neural Networks in pattern recognition, ANNPR 2016

ISBN: (纸本)9783319461816

the proceedings contain 27 papers. the special focus in this conference is on Learning Algorithms, Architectures and Applications. the topics include: A spiking neural network for personalised modelling of electrogastrography;improving generalization abilities of maximal average margin classifiers;finding small sets of random Fourier features for shift-invariant kernel approximation;incremental construction of low-dimensional data representations;soft-constrained nonparametric density estimation with artificial neural networks;interpretable classifiers in precision medicine;on the evaluation of tensor-based representations for optimum-path forest classification;on the harmony search using quaternions;learning parameters in deep belief networks through firefly algorithm;towards effective classification of imbalanced data with convolutional neural networks;on CPU performance optimization of restricted Boltzmann machine and convolutional RBM;comparing incremental learning strategies for convolutional neural networks;approximation of graph edit distance by means of a utility matrix;time series classification in reservoir- and model-space;objectness scoring and detection proposals in forward-looking sonar images with convolutional neural networks;background categorization for automatic animal detection in aerial videos using neural networks;predictive segmentation using multichannel neural networks in Arabic OCR system;quad-tree based image segmentation and feature extraction to recognize online handwritten bangla characters;using radial basis function neural networks for continuous and discrete pain estimation from bio-physiological signals;emotion recognition in speech with deep learning architectures and on gestures and postural behavior as a modality in ensemble methods.

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Constructing stochastic pyramids by MIDES – maximal independent directed edge set

Constructing stochastic pyramids by MIDES – maximal indepen...

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4th IAPR international workshop on graph based representations in pattern recognition, GbRPR 2003

作者： Haxhimusa, Yll Glantz, Roland Kropatsch, Walter G. Pattern Recognition and Image Processing Group 183/2 Institute for Computer Aided Automation Vienna University of Technology Favoritenstrasse 9 ViennaA-1040 Austria Dipartimento di Informatica Università di Ca’ Foscari di Venezia Via Torino 155 MestreVE30172 Italy

ISBN: (纸本)354040452X

We present a new method (MIDES) to determine contraction kernels for the construction of graph pyramids. Experimentally the new method has a reduction factor higher than 2.0. thus, the new method yields a higher reduction factor than the stochastic decimation algorithm (MIS) and maximal independent edge set (MIES), in all tests. this means the number of vertices in the subgraph induced by any set of contractible edges is reduced to half or less by a single parallel contraction. the lower bound of the reduction factor becomes crucial with large images. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Directed graphs

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