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42nd German Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics workshop on Visual Computing for Biology and Medicine, VCBM 2020

42nd German Conference on Pattern Recognition, DAGM GCPR 202...

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42nd German Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics workshop on Visual Computing for Biology and Medicine, VCBM 2020

ISBN: (纸本)9783030712778

the proceedings contain 34 papers. the special focus in this conference is on pattern recognition. the topics include: Adversarial Synthesis of Human Pose from Text;long-Term Anticipation of Activities with Cycle Consistency;multi-stage Fusion for One-Click Segmentation;neural Architecture Performance Prediction Using graph Neural Networks;discovering Latent Classes for Semi-supervised Semantic Segmentation;Riemannian SOS-Polynomial Normalizing Flows;automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning;Does SGD Implicitly Optimize for Smoothness?;Looking Outside the Box: the Role of Context in Random Forest based Semantic Segmentation of PolSAR Images;semantic Bottlenecks: Quantifying and Improving Inspectability of Deep representations;haar Wavelet based Block Autoregressive Flows for Trajectories;center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding;constellation Codebooks for Reliable Vehicle Localization;towards Bounding-Box Free Panoptic Segmentation;proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks;unsupervised Part Discovery by Unsupervised Disentanglement;on the Lifted Multicut Polytope for Trees;conditional Invertible Neural Networks for Diverse Image-to-Image Translation;image Inpainting with Learnable Feature Imputation;4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving;bias Detection and Prediction of Mapping Errors in Camera Calibration;inline Double Layer Depth Estimation with Transparent Materials;a Differentiable Convolutional Distance Transform Layer for Improved Image Segmentation;PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images;self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models;multimodal Semantic Forecasting based on Conditional Generation of Future Features;learning to Identify Physical Parameters from Video Using Differentiable

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Hybrid methodology focused on the model of binary patterns and the theory of fuzzy logic for facial biometric verification and identification 7

Hybrid methodology focused on the model of binary patterns a...

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7th international Joint Conference on Computational Intelligence, IJCCI 2015

作者： Nava, Sergio González Silva, Alberto J. Rosales Montiel, Nidiyare Hevia Funes, Francisco J. Gallegos González, Mario Dehesa Escuela Superior de Ingenieria Mecánica y Eléctrica Instituto Politécnico Nacional Zacatenco Ciudad de México Mexico Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de México Mexico

ISBN: (纸本)9789897581571

It is proposed a methodology to improve verification and identification biometric facial indicators based in hybridization binary pattern models and the fuzzy logic theory, making besides use of the traditional image pre-procebing models, feature extraction and clabifiers to validate the performance of the proposal methodology. the facial recognition is complicated due to the variability of the facial appearance related the same person, and the small characteristic samples for each person in adverse conditions. To fix this, is considered the binary pattern models as an excellent choice to the local face representations, whose more important properties is their tolerance against the variations of luminance, scale and rotation. However, the binary pattern model is sensitive to small variations of the pixel intensities, generally caused by the noise, which introduce uncertainty to the texture and contrast representation of the facial image. Using fuzzy logic in the binary patterns calculation, leads to a texture representation model that takes into account the uncertainty of the contained information in each image, providing a better representation of texture and contrast measure. In combination with traditional algorithms in the pre-procebing stage, as photometric and histogram normalization, the feature extraction stage is achieved using linear discriminants and Gabor wavelets to provide finally a stage of the support vector machines clabification. © Copyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Binary patterns Facial Biometrics Fuzzy Logic Image Procebing pattern recognition

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Consistent set estimation in k-dimensions: An efficient approach 7th

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7th Joint IAPR international workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd international workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Chaudhuri, A. Ray Basu, A. Bhandari, S.K. Chaudhuri, B.B. Indian statistical institute 203 B.T. Road Calcutta700 035 India

ISBN: (纸本)3540648585

Determining the shape of a point pattern is a problem of considerable practical interest and has applications in many branches of science related to pattern recognition. Set estimators of a nonparametric nature which may be used as shape descriptors should have several desirable properties. the estimators should be (a) consistent, i.e. Lebesgue measure of the symmetric difference of the actual region and the estimated should go to zero in probability;(b) computationally efficient;and (c) automatic, in the sense that the method should be able to detect the number of independent disjoint components in the region even when this number is unknown. None of the currently known estimators combine all these properties. A new shape descriptor called s-shape in the context of perceived border extraction of dot patterns in 2-D has been recently proposed. Here, a class of set estimators based on the s-shape is developed in k-dimensions, which combine all the above properties. these estimators are consistent not just under the uniform distribution, but also when samples are drawn under any continuous distribution. the order of error in estimation is independent of the dimensionality k. To illustrate the effectiveness of the proposed approach, a linear order algorithm readily derived from the definition is applied in digital domain. the role of t5 that controls the structure of the estimator, is analyzed. © Springer-Verlag Berlin Heidelberg 1998.

关键词： pattern recognition

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On the combination of ridgelets descriptors for symbol recognition

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7th international workshop on graphics recognition, GREC 2007

作者： Terrades, O. Ramos Valveny, E. Tabbone, S. Computer Vision Center Spain Dept. Computer Science Universitat Autònoma de Barcelona Spain LORIA-Université Nancy 2 France

ISBN: (纸本)3540881840

In this paper we propose an original solution to combine the scales of multi-resolution shape descriptors. More precisely, a classifier fusion scheme is applied to a set of shape descriptors obtained from the ridgelets transform. the Ridgelets coefficients are grouped into different descriptors according to their resolution. then a classifier is trained for each descriptor and a final classification is obtained using the classifier fusion scheme. We have applied this approach to symbol recognition using the GREC 2003 database. In this perspective, we increase the recognition rates of previous works on ridgelets-based descriptors. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： pattern recognition

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A graph theoretic approach to protein structure selection

A graph theoretic approach to protein structure selection

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7th international workshop on Fuzzy Logic and Applications

作者： Vassura, Marco Margara, Luciano Fariselli, Piero Casadio, Rita Univ Bologna Dept Comp Sci Bologna Italy Univ Bologna Dept Biol Biocomp Grp I-40127 Bologna Italy

ISBN: (纸本)9783540733997

Protein Structure Prediction (PSP) aims to reconstruct the 3D structure of a given protein starting from its primary structure (chain of amino acids). It is a well known fact that the 3D structure of a protein only depends on its primary structure. PSP is one of the most important and still unsolved problems in computational biology. Protein Structure Selection (PSS), instead of reconstructing a 3D model for the given chain, aims to select among a given, possibly large, number of 3D structures (called decoys) those that are closer (according to a given notion of distance) to the original (unknown) one. Each decoy is represented by a set of points in R-3. Existing methods for solving PSS make use of suitably defined energy functions which heavily rely on the primary structure of the protein and on protein chemistry. In this paper we present a completely different approach to PSS which does not take advantage at all of the knowledge of the primary structure of the protein but only relies on the graph theoretic properties of the decoys graphs (vertices represent amino acids and edges represent pairs of amino acids whose euclidean distance is less than or equal to a fixed threshold). Even if our methods only rely on approximate geometric information, experimental results show that some of the graph properties we adopt score similarly to energy-based filtering functions in selecting the best decoys. Our results show the principal role of geometric information in PSS, setting a new starting point and filtering method, for existing energy function-based techniques.

关键词： pattern recognition

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Overview of Topic-based Chinese message polarity classification in SIGHAN 2015 8

Overview of Topic-based Chinese message polarity classificat...

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8th SIGHAN workshop on Chinese Language Processing, SIGHAN 2015

作者： Liao, Xiangwen Li, Binyang Xu, Liheng College of Mathematics and Computer Science Fuzhou University China School of Information Science and Technology University of International Relations China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781941643570

this paper presents the overview of Topic-based Chinese Message Polarity Classification in SIGHAN 2015 bake-off. Topic-based message polarity classification plays an important role in sentiment analysis, information extraction, event tracking, and other related research areas. this task is designed to evaluate the techniques for Chinese message polarity classification towards a given topic. the task organizers manually constructed 25 topics together with 24,374 corresponding messages which were annotated to construct the training and testing datasets. the evaluation results achieved by the participators provide good suggestion for the future research. © 2015 Proceedings of the 8th SIGHAN workshop on Chinese Language Processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th international Joint Conference on Natural Language Processing, ACL IJCNLP 2015. All rights reserved.

关键词： Sentiment analysis

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A Comparative Study of Surface representations Used in Statistical Human Models

A Comparative Study of Surface Representations Used in Stati...

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7th international Conference on Articulated Motion and Deformable Objects (AMDO)

作者： Neophytou, Alexandros Hilton, Adrian Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 5XH Surrey England

ISBN: (纸本)9783642315671

this paper presents a quantitative and qualitative analysis of surface representations used in recent statistical models of human shape and pose. Our analysis and comparison framework is twofold. Firstly, we qualitatively examine generated shapes and poses by interpolating points in the shape and pose variation spaces. Secondly, we evaluate the performance of the statistical human models in the context of human shape and pose reconstruction from silhouette. the analysis demonstrates that body shape variation can be controlled with a lower dimensional model using a PCA basis in the Euclidean space. In addition, the Euclidean representation is shown to give more accurate shape estimates than other surface representations in the absence of pose variation. Furthermore, the analysis indicates that shape and pose parametrizations based on translation and rotation invariant representations are not robust for reconstruction from silhouette without pose initialization.

关键词： surface representations statistical models human shape and pose

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Sign Language Machine Translation Overkill 7

Sign Language Machine Translation Overkill

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7th international workshop on Spoken Language Translation, IWSLT 2010

作者： Stein, Daniel Schmidt, Christoph Ney, Hermann Human Language Technology and Pattern Recognition RWTH Aachen University Germany

Sign languages represent an interesting niche for statistical machine translation that is typically hampered by the scarceness of suitable data, and most papers in this area apply only a few, well-known techniques and do not adapt them to small-sized corpora. In this paper, we will propose new methods for common approaches like scaling factor optimization and alignment merging strategies which helped improve our baseline. We also conduct experiments with different decoders and employ state-of-the-art techniques like soft syntactic labels as well as trigger-based and discriminative word lexica and system combination. All methods are evaluated on one of the largest sign language corpora available. © IWSLT 2010. All rights reserved.

关键词： Computer aided language translation

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Articulated Motion and Deformable Objects - 7th international Conference, AMDO 2012, Proceedings

Articulated Motion and Deformable Objects - 7th Internationa...

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7th international Conference on Articulated Motion and Deformable Objects, AMDO 2012

ISBN: (纸本)9783642315664

the proceedings contain 28 papers. the topics discussed include: user identification and object recognition in clutter scenes based on RGB-depth analysis;spatial measures between human poses for classification and understanding;real-time pose estimation using constrained dynamics;an NVC emotional model for conversational virtual humans in a 3D chatting environment;an event-based architecture to manage virtual human non-verbal communication in 3D chatting environment;improving gestural communication in virtual characters;multi-view body tracking with a detector-driven hierarchical particle filter;real-time multi-view human motion tracking using particle swarm optimization with resampling;a comparative study of surface representations used in statistical human models;combining skeletal pose with local motion for human activity recognition;and a new marker-less 3D kinect-based system for facial anthropometric measurements.

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Partial Discharge pattern recognition with Data Augmentation based on Generative Adversarial Networks 7

Partial Discharge Pattern Recognition with Data Augmentation...

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7th international Conference on Condition Monitoring and Diagnosis, CMD 2018

作者： Wang, Xuhong Huang, Hongyi Hu, Yue Yang, Yupu Ministry of Education Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538641262

pattern recognition of partial discharge (PD) has various beneficial applications in academia and industry. However, it is hard and expensive to obtain extensively annotated PD data to build a high-performance classification model. Given there is potential to use PD data more effectively, this paper presents a novel data generative model based on generative adversarial networks (GAN). GAN are able to learn deep feature representations of existing PD signals and synthesizes more extensive UHF PD signals. An original PD data set of UHF PD signals is established by the partial discharge experiment. With the result of the original PD signals dataset as baseline, we evaluated the performance of some classifiers on the augmented dataset. the results show that UHF PD classification model benefits from GAN-based data augmentation techniques. Clearly, the GAN-based model has good potential in industry diagnosis of PD, especially when the data are sparse. © 2018 IEEE.

关键词： Generative adversarial networks

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