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12th international conference on pattern recognition applications and methods, icpram 2023

ISBN: (纸本)9783031547256

the proceedings contain 8 papers. the special focus in this conference is on pattern recognition applications and methods. the topics include: Real-World Indoor Location Assessment with Unmodified RFID Antennas;MSAA-Net: Multi-Scale Attention Assembler Network Based on Multiple Instance Learning for Pathological Image Analysis;analysis of Generative Data Augmentation for Face Antispoofing;improving Person Re-identification through Low-Light Image Enhancement;gender-Aware Speech Emotion recognition in Multiple Languages;pattern recognition Techniques in Image-Based Material Classification of Ancient Manuscripts.

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pattern recognition applications and methods: 4th international conference, icpram 2015 Lisbon, Portugal, January 10–12, 2015 revised selected papers 4th

Pattern recognition applications and methods: 4th internatio...

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4th international conference on pattern recognition applications and methods, icpram 2015

作者： Fred, Ana De Marsico, Maria Figueiredo, Mário Instituto de Telecomunicações Instituto Superior Técnico University of Lisbon Lisbon Portugal Sapienza Università di Roma Rome Italy

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10th international conference on pattern recognition applications and methods, icpram 2021 and 11th international conference on pattern recognition applications and methods, icpram 2022

10th International Conference on Pattern Recognition Applica...

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10th international conference on pattern recognition applications and methods, icpram 2021 and 11th international conference on pattern recognition applications and methods, icpram 2022

ISBN: (纸本)9783031245374

the proceedings contain 8 papers. the special focus in this conference is on pattern recognition applications and methods. the topics include: Retinotopic Image Encoding by Samples of Counts;gesture recognition and Multi-modal Fusion on a New Hand Gesture Dataset;Reduced Precision Research of a GAN Image Generation Use-case;similarity Constrained Conditional Generative Auto-encoder with Generalized Dilated Networks;perusal of Camera Trap Sequences Across Locations;preface.

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Gender-Aware Speech Emotion recognition in Multiple Languages 12th

Gender-Aware Speech Emotion Recognition in Multiple Language...

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12th international conference on pattern recognition applications and methods (icpram)

作者： Nicolini, Marco Ntalampiras, Stavros Univ Milan Dept Comp Sci Milan Italy

ISBN: (纸本)9783031547256;9783031547263

this article presents a solution for Speech Emotion recognition (SER) in multilingual setting using a hierarchical approach. the approach involves two levels, the first level identifies the gender of the speaker, while the second level predicts their emotional state. We evaluate the performance of three classifiers of increasing complexity: k-NN, transfer learning based on YAMNet, and Bidirectional Long Short-Term Memory neural networks. the models were trained, validated, and tested on a dataset that includes the big-six emotions and was collected from well-known SER datasets representing six different languages. Our results indicate that there are differences in classification accuracy when considering all data versus only female or male data, across all classifiers. Interestingly, prior knowledge of the speaker's gender can improve the overall classification performance.

关键词： Audio pattern recognition Machine learning Transfer learning Convolutional neural network YAMNet Multilingual speech emotion recognition

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Distance Transform in Images and Connected Plane Graphs 12th

Distance Transform in Images and Connected Plane Graphs

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12th international conference on pattern recognition applications and methods (icpram)

作者： Banaeyan, Majid Kropatsch, Walter G. TU Wien Pattern Recognit & Image Proc Grp Vienna Austria

ISBN: (纸本)9783031547256;9783031547263

the distance transform (DT) serves as a crucial operation in numerous image processing and pattern recognition methods, finding broad applications in areas such as skeletonization, map-matching robot self-localization, biomedical imaging, and analysis of binary images. the concept of DT computation can also be extended to non-grid structures and graphs for the calculation of the shortest paths within a graph. this paper introduces two distinct algorithms: the first calculates the DT within a connected plane graph, while the second is designed to compute the DT in a binary image. Both algorithms demonstrate parallel logarithmic complexity of O(log(n)), with n representing the maximum diameter of the largest region in either the connected plane graph or the binary image. To attain this level of complexity, we make the assumption that a sufficient number of independent processing elements are available to facilitate massively parallel processing. Both methods utilize the hierarchical irregular pyramid structure, thereby retaining topological information across regions. these algorithms operate entirely on a local level, making them conducive to parallel implementations. the GPU implementation of these algorithms showcases high performance, with memory bandwidth posing the only significant constraint. the logarithmic complexity of the algorithms boosts execution speed, making them particularly suited to handling large images.

关键词： Distance transform Connected plane graph Parallel logarithmic complexity Irregular graph pyramids Parallel processing

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REAL-TIME FACIAL-FEATURE TRACKING BASED ON MATCHING TECHNIQUES AND ITS applications 12

REAL-TIME FACIAL-FEATURE TRACKING BASED ON MATCHING TECHNIQU...

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conference B on pattern recognition and Neural Networks, at the 12th IAPR international conference on pattern recognition

作者： SAKO, H WHITEHOUSE, M SMIth, A SUthERLAND, A TRINITY COLL DUBLIN OREILLY INSTHITACHI EUROPE LTDCTR RES & DEVDUBLIN LABDUBLIN 2IRELAND

ISBN: (纸本)0818662700

this paper describes a method of real-time facial-feature extraction which is based on matching techniques. the method is composed of facial-area extraction and mouth-area extraction using colour histogram matching, and eye-area extraction using template matching. By the combination of these methods, we can realize real-time processing, user-independent recognition and tolerance to changes of the environment. Also, this paper touches on neural networks which can extract characteristics for recognizing the shape of facial parts. the methods were implemented in an experimental image processing system, and we discuss the cases that the system is applied to man-machine interface using facial gesture and to sign language translation. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Extraction

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pattern classification with missing data: a review

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NEURAL COMPUTING & applications 2010年第2期19卷 263-282页

作者： Garcia-Laencina, Pedro J. Sancho-Gomez, Jose-Luis Figueiras-Vidal, Anibal R. Univ Politecn Cartagena Dpto Tecnol Informac & Comunicac Murcia 30202 Spain Univ Carlos III Madrid Dpto Teoria Senal & Comunicac Madrid 28911 Spain

pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. the aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

关键词： pattern classification Missing data Neural networks Machine learning

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Bird-Like Information Processing for AI-Based pattern recognition

Bird-Like Information Processing for AI-Based Pattern Recogn...

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12th Mexican international conference on Artificial Intelligence (MICAI)

作者： Pham, Tuan D. Univ Aizu Aizu Res Cluster Med Engn & Informat Ctr Adv Informat Sci & Technol Aizu Wakamatsu Fukushima 9658580 Japan

ISBN: (纸本)9781479926053;9781479926046

Artificial-intelligence (AI)-base pattern recognition is of particular interests to many scientific disciplines ranging from life science to engineering. Practical applications of pattern or object recognition methods are numerous but still encountering many problems including the inherent difficulty in computerized feature extraction and classification. this paper proposes a strategy for object recognition resembling the active template matching strategy in birds. Experimental results on several databases suggest that using the active vision processing can improve classification rates implemented with various classifiers.

关键词： Bird-like information processing active vision AI-based pattern recognition

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pattern recognition Techniques in Image-Based Material Classification of Ancient Manuscripts 12th

Pattern Recognition Techniques in Image-Based Material Class...

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12th international conference on pattern recognition applications and methods (icpram)

作者： Dhali, Maruf A. Reynolds, thomas Alizadeh, Aylar Ziad Nijdam, Stephan H. Schomaker, Lambert Univ Groningen Dept Artificial Intelligence Groningen Netherlands Royal Holloway Univ London Dept Comp Sci London England

ISBN: (纸本)9783031547256;9783031547263

Classifying ancient manuscripts based on their writing surfaces often becomes essential for palaeographic research, including writer identification, manuscript localization, date estimation, and, occasionally, forgery detection. Researchers continually perform corroborative tests to classify manuscripts based on physical materials. However, these tests, often performed on-site, require actual access to the manuscript objects. these procedures involve specific expertise in manuscript handling, a considerable amount of time, and cost. Additionally, any physical inspection can accidentally damage ancient manuscripts that already suffer degradation due to aging, natural corrosion, and damaging chemical treatments. Developing a technique to classify such documents using noninvasive techniques with only digital images can be extremely valuable and efficient. this study uses images from a famous historical collection, the Dead Sea Scrolls, to propose a novel method to classify the materials of the manuscripts. the proposed classifier uses the two-dimensional Fourier transform to identify patterns within the manuscript surfaces. Combining a binary classification system employing the transform with a majority voting process is adequate for this classification task. this initial study shows a classification percentage of up to 97% for a confined amount of manuscripts produced from either parchment or papyrus material. In the extended work, this study proposes a hierarchical k-means clustering method to group image fragments that are highly likely to originate from a similar source using color and texture features calculated on the image patches, achieving 77% and 68% for color and texture clustering with 100% accuracy on primary material classification. Finally, this study explores a convolutional neural network model in a self-supervised Siamese setup with a large number of images that obtains an accuracy of 85% on the pretext task and an accuracy of 66% on the goal task to

关键词： Document analysis Image-based material analysis Historical manuscript Feature extraction Fourier transform Classification Clustering Convolutional neural network

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VLSI ARRAYS FOR SPEECH PROCESSING WIth LINEAR PREDICTIVE CODING 12

VLSI ARRAYS FOR SPEECH PROCESSING WITH LINEAR PREDICTIVE COD...

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conference C on Signal Processing and conference D on Parallel Computing, at the 12th IAPR international conference on pattern recognition

作者： TANG, YY LI, T SUEN, CY CONCORDIA UNIV CTR PATTERN RECOGNIT & MACHINE INTELLIGENCEMONTREALPQ H3G 1M8CANADA

ISBN: (纸本)0818662751

the covariance analysis of linear predictive coding has wide applications, especially in speech recognition and speech signal processing. Real-time applications demand very high processing speed for linear predictive coding analysis. VLSI technology which possesses properties of low-cost, high-speed and massive computing capabilities is a suitable candidate. In this paper, systolic array processors for the covariance analysis of linear predictive coding are developed. the covariance analysis of linear predictive coding contains a large set of irregular and nested recurrence equations. Systolizing the algorithm is a difficulty task for such a complex problem. Existing methods of systematic design for systolic arrays are not much helpful to this problem. To overcome it, a break-combination method is presented in this paper. In this manner, the task is first decomposed and then mapped onto several interconnected systolic arrays. the resulting systolic arrays of the sub problems are then combined to form a complete solution. © 1994 IEEE.

关键词： Systolic arrays

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