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共 5156 条记录，以下是271-280 订阅

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Using Hash and Deep Learning for Content-Based image Retrieval 6th

Using Hash and Deep Learning for Content-Based Image Retriev...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Hung, Bui thanh thuy, Tran Xuan Tam, Le Duy Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue to face challenges in processing speed. If these issues are resolved, image search has the potential to fully replace traditional search methods. the drawback of prior methods lies in lengthy feature vectors, resulting in time-consuming searches and inconvenience. To mitigate these issues, we employ feature extraction through convolutional neural networks and subsequently apply binary hashing. Experimental results on the fashion-product-images dataset show that using a convolutional neural network combined with binary hashing in image search achieves a mean average precision (MAP 20) of 0.99 and significantly improves the image retrieval time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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An improved YOLOv8 safety helmet wearing detection network 4

An improved YOLOv8 safety helmet wearing detection network

引用

4th international conference on computer vision and pattern Analysis, ICCPA 2024

作者： Zhang, Tiankai Song, Xudong School of Computer and Communication Engineering Dalian Jiaotong University Liaoning Dalian116028 China

ISBN: (纸本)9781510682528

In the field of industrial safety, wearing helmets plays a vital role in the protection of workers. Aiming at the complex background in the industrial environment, caused by the difference in distance, the helmet small target wear detection of misdetection, omission detection problem, an improved YOLOv8 safety helmet wearing detection network is proposed, which aims to enhance the capture of details, improve multi-scale feature processing and improve the accuracy of small target detection by introducing DWR(Dilation-wise Residual) attention module, ASPP(Atrous Spatial Pyramid Pooling) pool pyramid. Experiments are conducted on the SHWD dataset, and the results show that the mAP of the improved network is enhanced to 92.0%, and it exceeds the traditional target detection network in terms of accuracy, recall, and other key metrics, which further improves the detection of helmet wearing in complex environments, and greatly enhances the accuracy of detection. © 2024 SPIE.

关键词： Wear of materials

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Kinship Verification Using Multiscale Retinex Preprocessing and Integrated 2DSWT-CNN Features 8

Kinship Verification Using Multiscale Retinex Preprocessing ...

引用

8th IEEE international conference on image and Signal processing and their Applications, ISPA 2024

作者： Belabbaci, El Ouanas Khammari, Mohammed Chouchane, Ammar Ouamane, Abdelmalik Bessaoudi, Mohcene University of Bejaia LIMED Laboratory Faculty of Technology Bejaia06000 Algeria University of Bejaia LIMED Laboratory Faculty of Exact Sciences Bejaia06000 Algeria University Center of Barika Faculty of Sciences and Technology Barika05001 Algeria University Mohamed Khider of Biskra Laboratory of LI3C Biskra Algeria

ISBN: (纸本)9798350309249

Kinship verification from facial images presents a challenging yet intriguing problem within the fields of pattern recognition and computer vision. In this study, we introduce significant advancements by applying a preprocessing technique known as Multiscale Retinex with Color Restoration (MSRCR) to enhance the quality and contrast of images. Our methodology uniquely combines the strengths of both deep and shallow feature descriptors by integrating them at the score level through the use of Logistic Regression (LR). Specifically, we utilize a novel descriptor, Histograms of a Two-Dimensional Stationary Wavelet Transform (Hist-2D-SWT), to capture non-deep features, while employing the CNN model for deep feature extraction. the efficacy of our approach is thoroughly evaluated through extensive experiments conducted on three challenging kinship datasets: Cornell KinFace, UB KinFace, and TS KinFace. © 2024 IEEE.

关键词： image enhancement

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Machine Learning Driven Exploration and Identification of Steel Surfaces: Leveraging Advanced computer vision Techniques 4

Machine Learning Driven Exploration and Identification of St...

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4th international conference on Multimedia processing, Communication and Information Technology, MPCIT 2024

作者： Dhiviya Bharathi, D. Varsha, Lanka Khushalini, Parvathagari Amruta, G.S. Joseph Abraham Sundar, K. Rajappa, Muthaiah School of Computing SASTRA Deemed to be University Tamil Nadu Thanjavur India

ISBN: (纸本)9798350375466

Steel surface flaw detection is a critical challenge for industries worldwide that have adopted manufacturing quality management. Conventional inspection methods are time-consuming, prompting the exploration of image analysis and machine learning techniques for efficient fault detection. this study aims to develop models for detecting steel defects using the SEVERSTAL dataset, which comprises 12,568 images of steel surfaces. Models were initially created for binary categorization (defect and no defect) and subsequently evaluated for multi-label classification. Additionally, the U-Net architecture was utilized to improve the accuracy of defect detection. Test-time augmentation was applied to further enhance the models' performance. the results demonstrate the effectiveness of the proposed approach in accurately detecting steel defects, highlighting the potential of machine learning and deep learning techniques in improving fault detection processes in the steel industry. © 2024 IEEE.

关键词： Quality management

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Research on Optimization of E-Commerce Service System Based on computer 3D image vision Technology 4

Research on Optimization of E-Commerce Service System Based ...

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4th international conference on computers, Information processing and Advanced Education, CIPAE 2023

作者： Zhang, Jun Ma, Lisha Weifang Engineering Vocational College Institute of Agriculture and Forestry Technology Qingzhou China

ISBN: (纸本)9798350342710

this paper builds an e-commerce operation service system which includes interface layer, core function layer, business process layer, service layer and resource layer Based on computer 3D image vision technology based on computer 3D image vision technology. then the paper proposes a simplified 2D human hand model, which models the human hand as a palm point and 5 fingers, and design a human hand tracking method based on particle swarm optimization (PSO) algorithm. the system uses Web server selection algorithm (RFWS) based on optimal request forwarding. A load balancing solution of e-business service system based on RFWS is constructed. the experimental simulation shows that the scheme is feasible and effective, and can greatly improve the access efficiency of users. © 2023 IEEE.

关键词： Electronic commerce

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Multi-task Multimodal Sentiment Analysis Based on Visual Data Mining 4

Multi-task Multimodal Sentiment Analysis Based on Visual Dat...

引用

4th international conference on Electronic Information Engineering and computer Science, EIECS 2024

作者： Gu, Huimin Jin, Guozhe Zhao, Yahui Cui, Rongyi Inst. of Intelligent Information Processing Yanbian University Yanji133002 China

ISBN: (纸本)9798331531409

In comparison to unimodal data, multimodal data provides a greater range of features, improving the model's effectiveness in sentiment analysis. However, effectively extracting and integrating emotional features from these diverse modalities remains a significant challenge. Heterogeneity and noise present in both visual and textual data frequently result in feature shifts and information loss, which can compromise the stability and precision of sentiment recognition. In response to this challenge, this paper introduces a new multimodal sentiment analysis model referred to as MVDM. First, we extract semantic information from visual elements, including generating image scene caption and recognizing text embedded in the image. then, we introduce a gating mechanism and a text-driven sparse attention module to denoise and fuse the multimodal features. Finally, we calculate the centroids of different emotion categories by accumulating multiple batches of samples, ensuring accurate alignment of emotional features over a broader range. Additionally, we employ a sliding-label cross-entropy loss strategy to further calibrate feature shift and reduce the risk of model overfitting. the results of Experimental demonstrate that MVDM significantly improves the accuracy and stability of multi-modal sentiment recognition. © 2024 IEEE.

关键词： Semantics

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Unconventional to Automated Attendance Marking Using image processing 4th

Unconventional to Automated Attendance Marking Using Image P...

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4th international conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Pradhan, Abhiujjwal Pal, Ritu Kumar, Sachin Koli, Sakshi Kalra, Bharti Nawaz, Waris Computer Science Department Tula’s Institute Dehradun Dehradun India

ISBN: (纸本)9789811998874

the traditional method of raising your hand in a classroom to say "present ma’am" or "yes ma’am" or whatever other things you would say is kind of fading away, image processing is becoming increasingly important in the digital world. Magicians play an important function in today's information era. Visual processing is necessary in the area of biometrics to identify a individual whose biometric image has already been saved in a database. image based biometrics, such as faces, biometrics, and eyes, need image processing and pattern recognition algorithms. To perform correctly, an image-based biometric system requires a sample image of the user's biometric that is highly clear and unadulterated. this study proposes the development of a system that automated stamps students’ attendance using automated image render ways such as facial detection and identification. Face recognition is challenging due to factors such as face pattern, orientation, shape, varied patterns, and the complexity of face expression. Using multiple datasets, the system is taught to identify the face-representing pattern (positive pattern of face) and set it apart from the surroundings (critical pattern of facial expression) environment. the major goal is to provide an autonomous platform for recognising faces in video, as well as documenting student attendance by identifying them from their various face patterns. this aids in the automatic maintenance and management of the attendance system. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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Contactless Gesture recognition Using Air Canvas 8th

Contactless Gesture Recognition Using Air Canvas

引用

8th international conference on ICT for Sustainable Development, ICT4SD 2023

作者： Gosavi, Janhavi Kadam, Neerajaa Shetty, Ameya Verekar, Anant Vishwakarma, Pinki Department of Computer Engineering Shah and Anchor Kutchhi College Mumbai India

ISBN: (纸本)9789819949311

the proposed work is a real-time implementation in Python for air-writing using algorithms like media pipe hands and easy OCR. Air writing has emerged as one of the most difficult study fields in image processing and pattern recognition in recent years. Air writing is the act of using one’s hands or fingers to scribble letters or words in a clear area. the proposed work aims at the implementation of a contactless gesture recognition software which uses an air canvas which sums up as providing an image input to the machine via a camera device, while the machine processes it and gives a delineated digital output of the work in real time, features such as optical character recognition, screenshot, and duplication will be an added attempt at closing the gap of limitations. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： computer vision

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Enhancing Automatic image Captioning: A Systematic Analysis of Models, Datasets, and Evaluation Metrics 4

Enhancing Automatic Image Captioning: A Systematic Analysis ...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ganesan, Kothai Amutha, S. Saketh, G. Venkata Reddy, G. Vishnu Vardhan Chowdary, G. Harinath Sai, J. Ajay Kpr institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331529635

the fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human intervention. this area of research has attracted considerable interest due to its potential uses in improving accessibility, managing content, and enhancing search engine optimization. Over the past years, there has been a drastic increase in the accuracy and detail of captions thanks to recent advances in deep learning, especially with convolutional neural networks (CNNs), long short-term memory (LSTMs), and recurrent neural networks (RNNs). this research examines cutting-edge approaches to automatic image captioning, including attention mechanisms, models based on transformers, and techniques utilizing reinforcement learning. the effectiveness of these methods was evaluated using benchmark datasets, and their advantages and limitations were assessed. Additionally, the obstacles faced in producing contextually appropriate and linguistically precise captions were explored, such as dealing with intricate scenes, uncommon objects, and abstract ideas. the paper concludes by highlighting potential areas for future research, including learning across multiple modalities, captioning across languages, and incorporating common-sense knowledge to improve caption generation. © 2024 IEEE.

关键词： Deep reinforcement learning

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Leveraging Deep Quantum Convolutional Neural Networks for Student Facial Expression Identifcation and Mode Assessments 4

Leveraging Deep Quantum Convolutional Neural Networks for St...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Hossain, Javed Alam, Md. Nur Akib, Mohammad Yasin Nur Kogoya, Krisdahim Nanjing University of Information Science and Technology Dept. of Artificial Intelligence Nanjing China Nanjing University of Information Science and Technology Dept. of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331529635

the incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. this study explores the potential of quantum computing in addressing these challenges through the development of a Deep Quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58 © 2024 IEEE.

关键词： Convolutional neural networks

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