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检索条件"任意字段=12th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2023"

共 89 条记录，以下是71-80 订阅

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An empirical comparison of in-learning and post-learning optimization schemes for tuning the support vector machines in cost-sensitive applications

An empirical comparison of in-learning and post-learning opt...

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international conference on Image Analysis and Processing

作者： F. Tortorella DAEIMI Università degli Studi di Cassino Italy

Support vector machines (SVM) are currently one of the classification systems most used in pattern recognition and data mining because of their accuracy and generalization capability. However, when dealing with very complex classification tasks where different errors bring different penalties, one should take into account the overall classification cost produced by the classifier more than its accuracy. It is thus necessary to provide some methods for tuning the SVM on the costs of the particular application. Depending on the characteristics of the cost matrix, this can be done during or after the learning phase of the classifier. In this paper we introduce two optimization schemes based on the two possible approaches and compare their performance on various data sets and kernels. the first experimental results show that both the proposed schemes are suitable for tuning SVM in cost-sensitive applications.

关键词： Support vector machines Costs Support vector machine classification pattern recognition Data mining Cancer Error correction Kernel Risk management Classification algorithms

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Outlier detection on mixed-type data: An energy-based approach 12th

Outlier detection on mixed-type data: An energy-based approa...

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12th international conference on Advanced Data Mining and applications, ADMA 2016

作者： Do, Kien Tran, Truyen Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9783319495859

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (***). the *** is a principled probabilistic method that models data density. We propose to use free-energy derived from *** as outlier score to detect outliers as those data points lying in low density regions. the method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of *** is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts. © Springer international Publishing AG 2016.

关键词： Free energy

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Deep Feature Learning for Image-Based Kinship Verification 40th

Deep Feature Learning for Image-Based Kinship Verification

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40th Computer Graphics international conference (CGI)

作者： Zhao, Shuhuan Wang, Chunrong Liu, Shuaiqi Cheng, Hongfang Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China Machine Vis Technol Innovat Ctr Hebei Baoding 071002 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR Beijing 100190 Peoples R China

ISBN: (纸本)9783031500688;9783031500695

Facial image-based kinship verification is one of the challenging tasks in computer vision. It has many potential applications, such as human trafficking, studying human genetics, generating family maps, family photo albums, etc. therefore, we propose a deep feature learning method (DFLKV) which can extract more discriminative features for kinship verification. For a pair of facial images, we firstly design a network with multi-scale channel attention for the features extraction;then, select four methods for feature fusion;finally, infer kinship based on the fused features. We jointly adopt the contrastive loss and the binary cross-entropy loss to compute matching degree for paired samples. the experimental results on four widely used datasets KinFaceW-I, KinFaceW-II, Cornell KinFace and TS KinFace to validate the effectiveness of our approach.

关键词： Kinship verification Deep feature learning Channel attention

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VLNet: A Multi-task Network for Joint Vehicle and Lane Detection 12th

VLNet: A Multi-task Network for Joint Vehicle and Lane Det...

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12th international conference on Image and Graphics, ICIG 2023

作者： Feng, Aiqi Liu, Haodong Xu, Tianyang Zhang, Donglin Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9783031463075

Visual perception is a crucial component of autonomous driving. Fully understanding the road environment is conducive to the safe driving of vehicles. However, most existing methods are usually unable to simultaneously accomplish multiple visual perception tasks, limiting the promotion and utilization. To this end, we propose VLNet: a novel and effective multi-task road environment perception network, which can perform vehicle and lane detection simultaneously. Specifically, VLNet consists of a shared feature extraction network and two detection branches for specific tasks. the vehicle detection branch uses a multi-scale method to detect vehicles, and the lane detection branch outputs instance-level segmentation map of lanes. Considering the correlation between vehicles and lanes, we leverage the semantic clues of the lane branch to assist vehicle detection. Compared with single-task networks, the proposed method reduces memory and computing costs effectively. Finally, experiments on the challenging Boxy vehicle detection dataset and the LLAMAS lane detection dataset demonstrate the efficacy of the developed model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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Structural, Syntactic, and Statistical pattern recognition 2008

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丛书名： Lecture Notes in Computer Science

2008年

作者： Niels da Vitoria Lobo Takis Kasparis Michael Georgiopoulos Fabio Roli James Kwok Georgios C. Anagnostopoulos Marco Loog

ISBN: (数字)9783540896890

ISBN: (纸本)9783540896883

this book constitutes the refereed proceedings of the 12th international Workshop on Structural and Syntactic pattern recognition, SSPR 2008 and the 7th international Workshop on Statistical Techniques in pattern recognition, SPR 2008, held jointly in Orlando, FL, USA, in December 2008 as a satellite event of the 19th international conference of pattern recognition, ICPR 2008. the 56 revised full papers and 42 revised poster papers presented together with the abstracts of 4 invited papers were carefully reviewed and selected from 175 submissions. the papers are organized in topical sections on graph-based methods, probabilistic and stochastic structural models for PR, image and video analysis, shape analysis, kernel methods, recognition and classification, applications, ensemble methods, feature selection, density estimation and clustering, computer vision and biometrics, pattern recognition and applications, pattern recognition, as well as feature selection and clustering.

关键词： pattern recognition systems Congresses.

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A deterministic algorithm for edge-preserving computed imaging using Legendre transform

A deterministic algorithm for edge-preserving computed imagi...

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international conference on pattern recognition

作者： G. Aubert M. Barlaud L. Blanc-Feraud P. Charbonnier Laboratoire J.A. Dieudonnb URA du Cnrs University de Nice Nice France Laboratoire URA du Cnrs Gdr Traitement du Signal et Images University of Nice-Sophia Antipolis La Valbonne France Laboratoire URA du Cnrs Gdr Traitement du Signal Images University of Nice-Sophia Antipolis La Valbonne France

Many image processing problems are ill-posed and must be regularized. Usually, a roughness penalty is imposed on the solution. the difficulty is to avoid the smoothing of edges, which are very important attributes of the image. We first propose sufficient conditions for the design of such an edge-preserving regularization. Using the Legendre transform, it is then possible to introduce an auxiliary variable which role is twofold. Firstly, it marks the discontinuities and ensures their preservation from smoothing. Secondly, it makes the criterion half-quadratic. the optimization is then easier. We propose a deterministic algorithm, based on alternate minimizations over the image and the auxiliary variable. We apply this algorithm to the problem of SPECT reconstruction.

关键词： Smoothing methods Image reconstruction Image processing Sufficient conditions Minimization methods Computer applications Gaussian noise Image restoration Single photon emission computed tomography Vectors

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Facial family similarity recognition using Local Gabor Binary pattern Histogram Sequence

Facial family similarity recognition using Local Gabor Binar...

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international conference on Hybrid Intelligent Systems (HIS)

作者： Mohammad Mahdi Dehshibi Jamshid Shanbezadeh Meysam Alavi Department of Computer Engineering Science and Research Branch I.A.U. Tehran Iran Computer Engineering Department Kharazmi University Karaj Iran Computer Engineering Department University of Science and Culture Hamedan Iran

Facial image analysis is one of the areas that have been received considerable attention in recent decades. In addition to areas such as face recognition, gender classification, emotion recognition, and age estimation, there are new applications that have not been studied yet. Family similarity recognition is a new trend that has been studied in this paper for the first time. Local Gabor Binary pattern Histogram Sequence (LGBPHS) has led to many important advances in face recognition, including over looking generalizability and training issues. Given the current status of this study, two approaches were considered: (1) holistic approach and (2) component-based approach, which embodies the practical principles of theory. In order to model facial family manifold, LGBP is used both for the holistic view and components of the face. For recognition, histogram intersection is used to measure the similarity of different LGBPHSes and the nearest neighborhood is exploited for final clustering. In order to prove the efficiency of the proposed method three set of experiments are conducted in which both subjective and algorithmic issues are considered. It is observed in the course of experiments that the proposed method outperforms the subjective test up to 15%, and outperforms the observed state of the art face recognition methods up to 25.06%.

关键词： Decision support systems Hybrid intelligent systems

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Towards Better Ocular recognition for Secure Real-World applications

Towards Better Ocular Recognition for Secure Real-World Appl...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Reza M. Parizi Ali Dehghantanha Kim-Kwang Raymond Choo Kennesaw State University Kennesaw GA US The University of Sheffield Sheffield Sheffield GB University of Texas at San Antonio San Antonio TX US

In the last decade, biometric has become one of the most common uses for verification and authentication of someone's personal identity. Biometrics are defined as the measurement and analysis of unique physical or behavioral characteristics. Fingerprint is undoubtedly the most well-known biometric, but in the recent years ocular recognition has taken a huge jump forward. Ocular recognition (OR) is one of the most reliable personal identification methods in biometrics that can have flexible applications as opposed to the iris recognition. the basic breakdown of the eye is the iris, cornea, retina, pupil, sclera, and conjunctiva. the iris is where eye is actually colored; this is determined by the amount and type of pigment in one's iris. Iris recognition is an accurate form of ocular recognition. It works by using biometric systems to apply pattern recognition to a person's eye since no two people have the same patterns in their eye. Ocular recognition has been used as an identification method on both Apple and Android devices without much popularity. the reason for this is mainly related to the issues with the underlying algorithm used that affects the accuracy and security measures. Many different algorithms have been proposed and tested; some are more accurate than others. Our goal is to investigate different technologies currently used to help combine the best parts of both (Iris and Retina) in order to propose a better system for future implementation. With the proper design, ocular recognition could be a great tool for user authentication in emerging Internet of things (IoT) and mobile markets.

关键词： Iris recognition Retina Authentication Fingerprint recognition Hardware Software

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Volumetric Image Reconstruction by 3D Hahn Moments

Volumetric Image Reconstruction by 3D Hahn Moments

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IEEE/ACS international conference on Computer Systems and applications

作者： Mostafa El Mallahi Abderrahim Mesbah Hassan Qjidaa Aissam Berrahou Khalid Zenkouar Hakim El Fadili CED-ST Center of Doctoral Studies in Sciences and Technologies LESSI laboratory Faculty of Sciences Dhar el Mehraz Sidi Mohamed Ben Abdellah University Fez Morocco Mohammadia School of Engineers Mohamed V University Rabat LSIA laboratory Faculty of Science Technique Sidi Mohamed Ben Abdellah University Fez ENSA National school of engineering Sidi Mohamed Ben Abdellah University Fez

ISBN: (纸本)9781509004799

three-Dimensional Hahn moments are performant tool in the domain of image processing applications and pattern classification. In this work, we propose a new method for computing the three-Dimensional Hahn moments. this method is based on matrix multiplication and symmetry property to decrease the complexity and computational time for volumetric image reconstruction. Experimental results showed that the proposed method is very efficient in terms of computation time, but also in terms of volumetric image reconstruction capability.

关键词： three-Dimensional Hahn moments Volumetric Image symmetry property matrix multiplication Matrix multiplication image reconstruction computational time Moment of force pattern recognition new methods

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A Dynamic Spatio-temporal Network with Self-attention for Multi-station Passenger Flow Prediction

A Dynamic Spatio-temporal Network with Self-attention for Mu...

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international conference on Awareness Science and Technology (iCAST)

作者： Fengzhi Wang Qinzhi Lv Lijuan Liu College of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding

Passenger flow prediction is vitally significant for intelligent transportation systems (ITS). Most of the studies typically focus on the passenger flow prediction for an individual station, and only capture the temporal features without considering any spatial features. Constructing a passenger flow prediction model for multiple stations, or even a whole network, is more valuable for practical applications. therefore, we develop a dynamic spatio-temporal network (DSTNet) with a self-attention (SA) mechanism for multi-station passenger flow prediction. A dynamic graph convolutional network (DGCN) is applied for the spatial feature extraction, and gated recurrent unit (GRU) is combined to learn the temporal features. SA is applied to further assign the weights for the extracted spatio-temporal features. the Experiment has been conducted on the passenger flow in the Xiamen bus rapid transit (BRT). the results demonstrate that the proposed DSTNet with SA (SA-DSTNet) outperforms the baselines in the multi-station passenger flow prediction task.

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