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检索条件"任意字段=3rd Iberian Conference on Pattern Recognition and Image Analysis"

共 1490 条记录，以下是1-10 订阅

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Architectural Decorative image pattern recognition in the Context of Iconography for Efficient Information Mining 3

Architectural Decorative Image Pattern Recognition in the Co...

引用

3rd International conference on Sentiment analysis and Deep Learning, ICSADL 2024

作者： Qin, Yu College of Art and Design Guangzhou Panyu Polytechnic Guangzhou City511483 China

ISBN: (纸本)9798350396157

This study addresses the crucial task of architectural decorative image pattern recognition in the context of iconography, with an emphasis on efficient information mining. The proposed research work presents a novel algorithm that combines edge detection, ResNet50, and the triplet attention mechanism for image style analysis. The proposed methodology is organized into comprehensive sections, including a literature review that explores various studies in image pattern recognition. The proposed algorithm focuses on overcoming the challenges faced by existing deep learning methods in capturing aesthetic style features. It introduces a multifaceted self-supervised task and incorporates edge detection using the Canny method, followed by the application of ResNet50 with a triplet attention mechanism. The paper then focuses on the optimization of the network structure for the targeted task of architectural decorative image recognition, introducing a feature embedding encoder (FEE) to effectively handle multi-level structures. In the experimental phase, the proposed model is tested against traditional CNN and FCM models, demonstrating superior performance with recognition accuracy consistently ranging from 97% to 99%. The comparative analysis highlights the effectiveness of the proposed algorithm in achieving high accuracy, positioning it as a promising solution for image recognition tasks. © 2024 IEEE.

关键词： Deep learning

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Research on Electricity Consumption Characteristic analysis and Load Identification Method Based on Symmetrized Dot pattern Information Fusion 3

Research on Electricity Consumption Characteristic Analysis ...

引用

3rd International conference on Energy and Electrical Power Systems (ICEEPS)

作者： Li, Le Liu, Zhiyuan Han, Hao Li, Chaoran Liu, Nan Zhu, Xiaoxun State Grid Beijing Daxing Elect Power Supply Co Sales Dept Beijing Peoples R China North China Elect Power Univ Dept Power Engn Baoding Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Based on multi-label load identification, power load feature analysis and intelligent identification method based on Symmetrized Dot pattern (SDP) information fusion. This method improves the decomposition efficiency and solves the problem of large reconstruction error;proposes the method of SDP fusion feature analysis, extracts the SDP image feature of each modal information of the load, improves the completeness of the information;proposes the SDP image recognition method based on YOLOv5 and builds on load intelligent recognition model. Through experimental research, the load identification accuracy of this method reached 98%, which ensured the level of non-invasive load monitoring.

关键词： load identification complementary ensemble mode decomposition symmetrized dot pattern YOLOv5 fusion features

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Grottoes color analysis based on image processing and its application in environmental design 24

Grottoes color analysis based on image processing and its ap...

引用

3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Zhang, Jing Han, Xinghui Zhao, Bo College of Art and Design Xian University of Technology China

ISBN: (纸本)9798400718120

This study aims to explore the grotto color analysis based on image processing and its application in environmental design. By using multispectral and hyperspectral imaging techniques, we collected and analyzed the color information of grotto murals in detail, applied to cultural heritage protection, modern architectural design and interior beautification. Color histogram, convolutional neural network (CNN), K-means clustering techniques were used to preprocess image data, feature extraction and pattern recognition. The results show that these techniques can effectively improve the accuracy of color restoration and provide scientific color application scheme for environmental design. © 2024 Copyright held by the owner/author(s).

关键词： Ecodesign

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A Parameter correction method of motor system based on particle swarm and convolutional neural network 3

A Parameter correction method of motor system based on parti...

引用

3rd conference on Fully Actuated System Theory and Applications (FASTA)

作者： Zeng, Depeng Hong, Junjie Han, Liangheng Zhang, Kun Ren, Yueru Wang, ZunHeng Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 15001 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

In order to generate accurate predictions in the vibration characteristics analysis of the motor structure, a motor system parameter correction method based on particle swarm optimization is proposed in this paper. Initially, focusing on the motor itself and utilizing modal analysis theory, finite element analysis software is employed for modal simulation analysis of the motor model. Subsequently, parameters such as shell stiffness, shell density, and spring stiffness between end caps and the shell are adjusted to better match the actual motor model. The particle swarm optimization algorithm is then used to optimize the structural parameters of the motor, obtaining a non-inferior optimal solution between the vibration performance and operational performance of the driving motor. At the same time, in order to reduce the manual workload, the image recognition model is established according to convolutional neural network to find out the target output image. Finally, the proposed method is subjected to simulation analysis. The results indicate that this strategy can to some extent achieve identification and optimization of motor system parameters. Additionally, the algorithm can reduce the complexity of modal calculations for motor systems and the image recognition model can find the target pattern accurately, demonstrating practical value.

关键词： particle swarm optimization convolutional neural network modal analysis

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Traffic pattern recognition Algorithm Based on Multi-Scale Feature Extraction 3

Traffic Pattern Recognition Algorithm Based on Multi-Scale F...

引用

3rd IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Zeng, Hao Sichuan Post and Telecommunication College Chengdu China

ISBN: (纸本)9798350317022

The characteristics of various traffic modes are analyzed, the fuzzy neural network is used to identify the traffic modes, the eigenvalues of the identified traffic modes are determined, and the correctness of the network is tested by traffic flow. The manual approaches significantly capture the spatial and temporal relationships as well as enhance the model complexity through examining both connected and unconnected roads. In feature extraction process, initially the input image is provided to the computation gradients process and processed output provided to the collect HOG over image and it provides the HOG feature. The test results show that the application of fuzzy neural network to traffic pattern recognition can accurately reflect the situation of traffic flow. Taking the results of traffic pattern recognition as the basis of road patency can guide the group control system to adopt corresponding strategies according to different traffic conditions. © 2023 IEEE.

关键词： feature extraction multiscale analysis traffic mode

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Research on image recognition Technology Based on CNN 3

Research on Image Recognition Technology Based on CNN

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Zhang, Hanqiao Senior High Beijing New Talent Academy Beijing101300 China

ISBN: (纸本)9781510667563

Convolution Neural Network (CNN) is an algorithm that takes an image as input, and then assigns weights and deviations to all aspects of the image to distinguish each other. The neural network can be trained by using a batch of images. Each image has a label to identify the true nature of the image. This paper gives the basic flow of CNN image recognition technology, and verifies the accuracy of its image recognition with the method of cat and dog image recognition. We used 1000 images for testing, and the recognition accuracy rate was as high as 94.5%, higher than the accuracy rate of 90.3% of SVM and 89.5% of KNN. The experimental results show that the image recognition technology based on CNN is a good image recognition technology, which can be widely used in real life. © 2023 SPIE.

关键词： image recognition

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Implementation of Face Feature Algorithms for Authentication of a Person (IFFAP) 3

Implementation of Face Feature Algorithms for Authentication...

引用

3rd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Ramachandra, A.C. Monisha, B. Muralapur, Poorvika Vasupraneetha, H. Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India

ISBN: (纸本)9798350318609

The present conventional digital financial transaction at Automated Teller Machines (ATM) is secured with PIN number, with this there are chances of forgetting or misusing to overcome this with the increase in technology and various advancements in financial infrastructure, most of the bank customers prefer to use Automated Teller Machines (ATM). To improve the security in such cases a new generation ATM machine which is based on face recognition technology is used. It also assists with changing passwords which increases privacy and security. The high-quality image has an important role in the recognition process. We propose Implementation of Face Feature Algorithms for Authentication of a Person (IFF AP) technique, an attempt to solve, The LBP algorithm and Haar Cascade techniques are employed for this process. This technology helps in increased security and avoids unauthorized transactions. This mutually benefits banking institutions and the customers. Leveraging existing algorithms, particularly those based on Local Binary pattern (LBP) and Haar Cascade, reveals a commendable qualitative analysis showcasing high accuracy up to 92.85% and improves computational efficiency compared to existing Techniques. © 2024 IEEE.

关键词： Local binary pattern

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Vehicle Theft Detection Using Neural pattern recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

引用

3rd International conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： image segmentation

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Advanced Optimization of Discrete Orthogonal Hahn Moments for Precision image Reconstruction via the Firefly Algorithm 3

Advanced Optimization of Discrete Orthogonal Hahn Moments fo...

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3rd International conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Bourzik, Abdelati Bouikhalene, Belaid El-Mekkaoui, Jaouad Hjouji, Amal Sultan Moulay Slimane University LIMATI Laboratory Beni Mellal Morocco Sidi Mohamed Ben Abdellah University LTI Laboratory Fez Morocco Sidi Mohamed Ben Abdellah University STIC Laboratory Fez Morocco

ISBN: (纸本)9798331522056

In recent decades, discrete orthogonal moments have frequently been used to represent images in a variety of computer vision and pattern recognition applications. These moments commonly employ discrete orthogonal polynomials as their basis, some of which are parametric and characterized by localization parameters. Optimizing these parameters is crucial for enhancing the efficiency of orthogonal moments in image analysis. This paper takes a new approach for optimizing the discrete Hahn polynomial parameters (\alpha, \beta) using the Firefly optimization algorithm, focusing on minimizing the mean square reconstruction error. Where our method identifies the optimal α and β values for constructing Hahn polynomials to achieve superior image moments. The results demonstrate that our Firefly algorithm-based method significantly improves image reconstruction quality, yielding lower reconstruction errors, particularly in low orders of Hahn moments. Furthermore, the proposed method outperforms both the artificial bee colony optimization method and the standard parameter selection method. The experiments clearly indicate the advantages of our proposed method, especially in the lower orders of moments. © 2024 IEEE.

关键词： image reconstruction

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Design and analysis of CNN based Residue Number System for Performance Enhancement 3

Design and Analysis of CNN based Residue Number System for P...

引用

3rd International conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Jency Rubia, J. Sherin Shibi, C. Babitha Lincy, R. Dhamodharan, S. K.Ramakrishnan College of Engineering Department Ece Tamil Nadu Trichy India Srm Institute of Science and Technology School of Computing Department of Ci Tamil Nadu Kattankulathur India Sri Eshwar College of Engineering Cce Department Tamil Nadu Coimbatore India

ISBN: (纸本)9781665462167

Convolutional Neural Network plays a vital role in Artificial intelligence and it's mainly harnessed for pattern recognition. However, it's quite tricky to achieve less image recognition time using the conventional CNN method. The conventional CNN module requires additional features that boost CNN performance in terms of delay. Thus, in this paper, the performance of the proposed system has been improved with the help of CNN-based Residue Number System (RNS). The performance of our designed CNN system is increased by reducing the delay in the Prewitt filter. Incorporating the proposed system in the Prewitt filter consumes less image recognition time. Further, the comparative analysis of the proposed work with other works for instance, the Binary Number System (BNS) and conventional RNS system has been performed. The accuracy of the recognition of an image is purely based on the numbers of test images. Therefore, the weight of the test image has been increased. But, the RNS scheme decreases the weight of a test image. Moreover, this comparative analysis investigates the better technique for delay minimization. Finally, the designed work is replicated and synthesized using MATLAB 2018b software. © 2023 IEEE.

关键词： image recognition

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