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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022"

共 265 条记录，以下是81-90 订阅

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Movement pattern recognition in Boxing Using Raw Inertial Measurements 3rd

Movement Pattern Recognition in Boxing Using Raw Inertial Me...

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3rd international conference on Optimization, learning Algorithms and Applications (OL2A)

作者： Puchalski, Radoslaw Giernacki, Wojciech Poznan Univ Tech Poznan Poland

ISBN: (纸本)9783031530357;9783031530364

In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificial neural network (ANN) model implemented on a microcontroller to classify boxing punches. Artificial intelligence (AI) enables the processing of sophisticated and complex patterns, and the X-CUBE-AI package allows the use of these possibilities in portable microprocessor systems. The input data to the network are linear accelerations and angular velocities read from the sensor mounted on the boxer's wrist. By using simple time-domain measurements without extracting signal features, the classification is performed in real-time. An extensive experiment was carried out for two groups with different levels of boxing skills. The developed model demonstrated high efficiency in the identification of individual types of blows.

关键词： activity recognition artificial intelligence artificial neural networks edge computing microcontrollers

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Person recognition using Gait Energy Image, MobileNetV3Small and machine learning 3

Person Recognition using Gait Energy Image, MobileNetV3Small...

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3rd IEEE international conference on Technology, Engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Suthar, Omprakash Katkar, Vijay Vaghela, Krunal Marwadi University Department of Computer Engineering Rajkot India Marwadi University Department of Information Technology Rajkot India

ISBN: (纸本)9798350397819

In the past few decades, gait as a biometric attribute has garnered a lot of attention for its application in a broad variety of security and privacy-related scenarios, including identity recognition and authentication. The deployment of surveillance cameras allows for the collection of Gait data, which is a non-invasive form of identification that is based on a biometric trait that can be seen from a distance and can be utilized for identifying reasons. It is difficult to create reliable automatic gait identification systems since Gait can be modified by several covariate parameters. Some of these covariate elements include garments, walking tempo, camera view angle. In this work, a method for recognizing people with a high degree of accuracy is demonstrated. This paper makes use of the Gait Energy Image, MobileNetV3Sma11 model, and machine learning to develop an approach to recognize person using Gait pattern. The paper includes experimental findings, including precision, recall, f1-score, and classification accuracy, all of which point to the effectiveness of the suggested method. © 2023 IEEE.

关键词： Gait analysis

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Incorporating a new relational feature in Arabic online handwritten character recognition

Incorporating a new relational feature in Arabic online hand...

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3rd international conference on Computer Vision Theory and Applications

作者： Izadi, Sara Suen, Ching Y. Ctr Pattern Recognit & Machine Intelligence Montreal PQ Canada

ISBN: (纸本)9789898111210

Artificial neural networks have shown good performance in classification tasks. However, models used for learning in pattern classification are challenged when the differences between the patterns of the training set are small. Therefore, the choice of effective features is mandatory for obtaining good performance. Statistical and geometrical features alone are not suitable for recognition of hand printed characters due to variations in writing styles that may result in deformations of character shapes. We address this problem by using a relational context feature combined with a local descriptor for training a neural network-based recognition system in a user-independent online character recognition application. Our feature extraction approach provides a rich representation of the global shape characteristics, in a considerably compact form. This new relational feature provides a higher distinctiveness and increases robustness with respect to character deformations. While enhancing the recognition accuracy, the feature extraction is computationally simple. We show that the ability to discriminate in Arabic handwriting characters is increased by adopting this mechanism in feed forward neural network architecture. Our experiments on Arabic character recognition show comparable results with the state-of-the-art methods for online recognition of these characters.

关键词： feature extraction online handwriting neural network recognition

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A self-organizing neural tree for large-set pattern classification 3

A self-organizing neural tree for large-set pattern classifi...

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3rd international conference on Document Analysis and recognition, ICDAR 1995

作者： Song, Hee-Heon Lee, Seong-Whan Department of Computer Science Chungbuk National University San 48 Gaeshin-dong Cheongju Chungbuk360-763 Korea Republic of Department of Computer Science Korea University 1 5-ka Anam-dong Seongbuk-ku Seoul136-701 Korea Republic of

ISBN: (纸本)0818671289

Neural networks have been successfully applied to various pattern classification problems owing to their learning ability, high discrimination power, and excellent generalization ability. However, for the case of classifying patterns which are large-set and require complex decision boundaries in high-dimensional pattern space, the greater part of conventional neural networks suffer from some of difficult problems to solve, such as the structure and size of the network, the computational complexity, and so on. In this paper, to cope with these difficulties, we propose a new self-organizing neural tree and its learning algorithm. The basic idea is to partition pattern space hierarchically using the tree-structured network composed of subnetworks with topology-preserving mapping ability. © 1995 IEEE.

关键词： learning algorithms

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The angle difference based state transition modeling for the signal pattern classification of the sensor array

The angle difference based state transition modeling for the...

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3rd international conference on Cybernetics and Information Technologies, Systems and Applications/4th international conference on Computing, Communications and Control Technologies

作者： Lee, SeungJae Kim, Changhwa Kim, Sangkyung Kim, Aram Kangnung Natl Univ Dept Comp Engn Gangnung Si 210702 Gangwon Do South Korea

ISBN: (纸本)9789806560840

Sensors have been used with various purposes in the human life. A sensor which can be functioned as a part of a signal process unit or a mechanical machine is defined as "a part of a measuring instrument which detects and responds immediately changes of a environment". As a sensor just reports the voltage level respect to detected physical or chemical quantity, it is needed to convert properly into meaning data. In most of cases, a sensor array, which consists of various kinds of sensors are used to detect a environment. There are two classes of methods to analyze signal patterns from a sensor array;the statistical method and the neural network method. One method has weak points comparing with another. One of each method's weak points is that most of statistical methods cannot consider shape characteristics of the signal pattern and neural network methods take too long time in the learning process. In spite of this weakness, the neural network process has been used in most of gas pattern recognition in recent studies. In this paper, we introduce a statistical method using state transition model for gas recognition. This paper focuses on making the accurate state transition model. We call this state transition model as ADSTM(Angle Difference based State Transition Model). Through various experiments, we analyze the proposed ADSTM modeling method. The results of experiments show that ADSTM is a fast and reliable statistical method for recognizing a signal pattern of the sensor array.

关键词： gas recognition gas sensor sensor array signal pattern state transition model

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network 3

Research on Intelligent Auxiliary Review of Power Grid 3D De...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Fan, Dongmei Yan, Wendou Liang, Yi Cheng, Zhi Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou102200 China

ISBN: (纸本)9798350319125

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid. © 2023 IEEE.

关键词： 3D model based on BP neural network computer Grid design power system

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SCHEMATA: 3D Classification and Categorization of Ancient Terracotta Figurines 12

SCHEMATA: 3D Classification and Categorization of Ancient Te...

引用

12th international conference on Agents and Artificial Intelligence (ICAART)

作者： Zeckey, Alexander Langner, Martin Georg August Univ Gottingen Inst Digital Humanities Heinrich Duker Weg 12 Gottingen Germany

ISBN: (纸本)9789897583957

The goal of the starting case-study is not only to develop procedures for automatically generating corpora using 3D pattern recognition, but also to reflect on the associated schematizations and how they can be applied in computer science and visual sciences. For this purpose, methods of object mining in 3D data are to be developed. We chose an object group which is defined by its complexity in shape and the similarity between the objects: In 4th and 3rd century BC ancient Greece small terracotta figurines used to be an art form that was quite common. Based on 200 of those terracottas, a classification system will be elaborated with digital methods, which is able to meet the complexity of the artefacts. In close cooperation between computer science and archaeology, this experimental process leads to a fundamental examination of the concept of pattern recognition as a humanities category. The discussion of the various concepts and methods will be carried out in two complementary dissertations.

关键词： Object Mining Image Exploration Greek Terracotta Figurines machine learning Typology

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3-D object recognition system using ultrasound 3

3-D object recognition system using ultrasound

引用

3rd international conference on Intelligent Sensing and Information Processing, ICISIP 2005

作者： Koley, C. Midya, B.L. IE Department Haldia Institute of Technology ICARE Complex HIT Campus Haldia India

ISBN: (纸本)0780395883

The patterns of ultrasonic reflected echoes from objects contain information about the geometric shape, size, orientation and the surface material properties of the reflector. Accurate estimation of the ultrasonic echo signal pattern is essential for recognition of the target object. We propose a method to classify different objects having specific geometric shape such as cylindrical, rectangular, sphere and conical of different size and material. Here Continuous Wavelet Transform (CWT) has been used for feature extraction. In the present work an attempt has been made to classify the pattern inherent in the features extracted through CWT of different echo signals with the help of two different machine learning algorithm like Self Organizing Feature Map (SOFM) and Support Vector machine (SVM). CWT allows a time domain signal to be transformed into time frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of feature from the non-stationary signals like ultrasonic echo envelop. SOFM transforms the input of arbitrary dimension into a one or two dimensional discrete map subject to a topological (neighbourhood preserving) constraint. In the present work the SOFM algorithm with Kohonen's learning and SVM in regression mode has been used to classify the patterns inherent in the features extracted through CWT of different echo envelop. © 2005 IEEE.

关键词： Support vector machines

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A rule learning method for academic document image processing 3

A rule learning method for academic document image processin...

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3rd international conference on Document Analysis and recognition, ICDAR 1995

作者： Takasu, Atsuhiro Satoh, Shin'ichi Katsura, Eishi Research and Development Department National Center for Science Information Systems 3-29-1 Otsuka Tokyo112 Japan Faculty of Design Tokyo University of Art and Design Tokyo192 Japan

ISBN: (纸本)0818671289

A new syntactic rule learning method is -presented for analyzing document images and constructing a database from them. This method is used in a digital library system named Cyber Magazine, where document images are sequentially converted into database tuples by block segmentation, rough classification, and syntactic analysis. The syntactic rule has an ability to analyze symbols located in two dimensional plane, and has a syntax similar to an ordinal context-frcc grammar except for the concatenation of symbols. In presented learning method, the syntactic rules are gen-erated from a set of parse trees by decomposing the trees according to non-terminal symbols, generalizing the decomposed trees to a syntactic rule, and merging them. © 1995 IEEE.

关键词： Image segmentation

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User recognition based on Gait pattern via Smartwatch Accelerometer in Unrestricted Environment 3

User Recognition based on Gait Pattern via Smartwatch Accele...

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3rd international conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Sheharyar Shah, Syed M Adnan Arsalan, Aamir Ahmed, Wakeel University of Engineering and Technology Taxila Department of Computer Science Taxila Pakistan Fatima Jinnah Women University Rawalpindi Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350332391

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature. © 2023 IEEE.

关键词： machine learning

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