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检索条件"任意字段=4th International Conference on Computer Vision, Pattern Recognition and Image Processing"

共 5158 条记录，以下是101-110 订阅

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Visual Insight: Deep Multilayer Fusion with Inception-based LSTM for Descriptive image Captioning 4

Visual Insight: Deep Multilayer Fusion with Inception-based ...

引用

4th international conference on computer Communication and Artificial Intelligence (CCAI)

作者： Khan, Rashid Huang, Bingding Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518188 Peoples R China Shenzhen Univ Coll Appl Sci Shenzhen 518060 Peoples R China

ISBN: (纸本)9798350362770;9798350362763

the image caption is a technology that aids us in comprehending the contents while employing machines to create descriptive text for an image. the captions are generated using Natural Language processing (NLP) and computer vision (CV). When the descriptions contain a single word like "boy," "cycle," etc., the image captioning work is completed by combining the detection method with image captioning when one predicted region covers the entire image, such as a boy riding a bicycle. to combine the tasks of localization and description It is presently a current hot trend in deep learning development to use it to analyze visual information and write descriptive text. this paper presented a multilayer dense focus image captioning model. We used transfer learning techniques to adjust pre-trained image classification models and integrate them with long short-term memory network (LSTM) architectures to evaluate the performance of each of the combined frameworks. the variable length input is encoded into a fixed-dimensional vector, which is taken as the maximum length of the caption available mapped with the image, and the recurrent neural network (RNN) uses this representation to "decode" it to the desired output sentence. We experimented with the Flickr8k, Flickr30k, VizWiz, and MSCOCO datasets. According to the analysis of experimental data on evaluation criteria, the model described in this research can effectively accomplish image captions according to the analysis of experimental data. Its performance is better than classic image captioning algorithms.

关键词： image captioning CNN Inception V3 LSTM Natural Language processing

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Uncertainty-aware bridge based mobile-former network for event-based pattern recognition 4

Uncertainty-aware bridge based mobile-former network for eve...

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4th international conference on Signal image processing and Communication, ICSIPC 2024

作者： Yang, Haoxiang Yuan, Chengguo Zhu, Yabin Chen, Lan Wang, Xiao Wang, Futian School of Computer Science and Technology Anhui University Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China Suzhou Glink IoT Technology Co. Ltd. Suzhou215000 China

ISBN: (纸本)9781510682467

the mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e.g., low illumination, motion blur). Meanwhile, the privacy protection issue caused by ultra-high definition (HD) RGB cameras aroused more and more people’s attention. Inspired by the success of event cameras which perform better on high dynamic range, no motion blur, and low energy consumption, we propose to recognize human actions based on the event stream. We propose a lightweight uncertainty-aware information propagation based Mobile-Former network for efficient pattern recognition, which aggregates the MobileNet and Transformer network effectively. Specifically, we first embed the event images using a stem network into feature representations, then, feed them into uncertainty-aware Mobile-Former blocks for local and global feature learning and fusion. Finally, the features from MobileNet and Transformer branches are concatenated for pattern recognition. Extensive experiments on multiple event-based recognition datasets fully validated the effectiveness of our model. the source code of this work will be released after the acceptance. © 2024 SPIE.

关键词： Photons

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Traffic Rules Violation Detection 4

Traffic Rules Violation Detection

引用

4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Meera, S. Sharmika Sree, R. Nithish Kumar, R. Hari Kishore, G. Sajin Kumar, P. Sri Sai ram Engineering College Computer Science and Engineering West Tambaram Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Around the world, bikers frequently forget to wear helmets, which can result in mishaps and fatalities. the detecting method is laborious and manual. the process will be automated by the suggested solution to address this issue. the technology uses image processing and machine learning methods to find people who are not equipped with helmets. An algorithm is used in the extraction of the license plate: picture collection, preliminary processing, edge detection and segmentation, feature extraction, and character recognition using an correct method. © 2024 IEEE.

关键词： image segmentation

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A Novel Framework for Human Action recognition Based on Features Fusion and Decision Tree 4

A Novel Framework for Human Action Recognition Based on Feat...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Bukht, Tanvir Fatima Naik Rahman, Hameedur Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9781665463775

image-based detection of human actions has recently emerged as a hot research area in the fields of computer vision and pattern recognition. It is concerned with detecting a person's actions or behavior from a static image. In this article, we are using a decision tree to develop an action recognition technique. In order to enhance the clarity of the video frames, the proposed method begins by implementing the HSI color transformation in the initial stage. Subsequently, it utilizes filters to minimize noise. the silhouette is extracted using a statistical method. We use SIFT and ORB for feature extraction. Next, using a parallel process, extract the shape and texture features needed for fusion applying name length control features. Additionally, the best high-dimensional data for classification is explored using vectors and the t-distributed stochastic neighbour embedding (t-SNE).the final step involves the features to be input into a decision tree, where they will be sorted into relevant human actions based on those final characteristics. the recognition rate of the UT interaction data used in the experimental process is 94.6%. © 2023 IEEE.

关键词： Decision trees

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A Survey: Traffic Sign recognition System using Learning Techniques 4

A Survey: Traffic Sign Recognition System using Learning Tec...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sharma, Priya Devare, Pratiksha Rasal, Ajinkya Kale, Shreyash Jadhav, Ganesh SKNCOE- Vadgaon Sppu Pune Department of Computer Engineering Pune India

ISBN: (纸本)9798331518981

A very important aspect of the development of smart transportation networks and road safety is 'traffic sign recognition'. this survey covers all types of learning techniques applied to traffic sign recognition with respect to effectiveness, challenges, and progress. the study begins with an overview of the foundational methods of traffic sign recognition: traditional image processing approaches and their limitations. Further below, the heart of the survey is modern learning techniques, especially 'machine learning (ML)' and 'deep learning (DL)' approaches. the report reviews employ learning techniques applied in 'traffic sign recognition systems'. It discusses both the most recent developments in deep learning and advanced machine learning as well as conventional image processing. the review ranges from classical techniques such as colour and shape-based recognition to modern techniques such as 'Convolutional Neural Network (CNN),' which have significantly increased recognition accuracy. Several important datasets on which the training and evaluation should be done have been reported. One of them is 'German Traffic Sign recognition Benchmark', and the challenges in practical applications along with the future research directions in this field have been highlighted. An overview has been provided regarding the effectiveness and evolution of learning techniques to improve 'Traffic sign recognition systems'. © 2024 IEEE.

关键词： Convolutional neural networks

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Fine-Grained image recognition Method Based on Input Perception Joint Probability Prediction 4

Fine-Grained Image Recognition Method Based on Input Percept...

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4th international conference on computer vision, image and Deep Learning, CVIDL 2023

作者： Yuan, Xiaoxi School of Information Wuhan Vocational College of Software and Engineering Wuhan430205 China

ISBN: (纸本)9798350326444

A method of fine-grained image recognition in the simple background based on input perception joint probability prediction is proposed. the recognition network consists of an input perception module and a joint uncertainty estimation module. the input perception module takes the input image as the global scale image and automatically extracts the foreground scale image and key component scale image by introducing the attention mechanism and using the foreground perception mechanism and component perception mechanism. the joint uncertainty estimation module uses three dynamic probability estimation parameters to evaluate the prediction confidence of each branch and then combines three branch votes to make the final decision. In order to verify the effectiveness and rationality of the input-aware and Probabilistic Prediction Convolutional Neural Network (IAPP-CNN) algorithm, a comparative experiment was performed on the data set. Ablation experiments verified the feasibility and rationality of the algorithm. the results show that the value of each evaluation index of the algorithm is more than 99.0%, which has high recognition accuracy and robustness and meets the recognition requirements. IAPP-CNN algorithm can improve the recognition accuracy of bird images with excellent performance and can greatly improve the performance of fine-grained image recognition. © 2023 IEEE.

关键词： image recognition

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Design and Development of Intelligent Inventory System for Small and Micro Enterprise Warehousing 4

Design and Development of Intelligent Inventory System for S...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Wang, Jinguang Li, Pengzhong Luo, Liang Sun, Hongbin Tongji University School of Mechanical and Energy Engineering Shanghai China

ISBN: (纸本)9798350373233

Warehousing for small and micro enterprises has been facing problems such as small warehousing scale, many types of materials, and more scattered placement locations. An intelligent warehousing inventory system was designed by combining visual recognition technology with the inventory module. the system uses image processing and machine vision technology to identify and decode the QR codes on materials and shelves, interact with the inventory module of the warehouse management system, detect whether the material and storage location matches and inventory the quantity of materials, and improve warehouse inventory level. the practical results show that the inventory accuracy of the intelligent inventory system is 98.5%, which can help small and micro enterprises better cope with the challenges in warehouse management, improve inventory efficiency, reduce labor costs, and enhance competitiveness. © 2023 IEEE.

关键词： computer vision

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Hybrid CNN and vision Transformer-Based Multi-Factor Authentication for Enhanced Security in Online Examination Systems 4

Hybrid CNN and Vision Transformer-Based Multi-Factor Authent...

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

作者： Reddy, Vallem Ranadheer Lingam, Gourishetty Shankar Ilanna, Peesala Kumar, Amgoth Ashok Chaitanya Deemed to be University Department of Computer Science and Engineering Kishanpur Telangana Hanamkonda506001 India Vaagdevi Engineering College Department of Computer Science and Engineering Bollikunta Telangana Warangal506005 India

ISBN: (纸本)9798331529635

With the rapid growth of online examination platforms, maintaining high levels of security, integrity, and user authentication is paramount. While existing methods utilize traditional security measures, the integration of advanced deep learning techniques can further improve the robustness of these systems. In this paper, we proposed a hybrid model that combines Convolutional Neural Networks (CNN) and vision Transformers (ViT) for facial recognition in a multi-factor authentication system for online examinations. the CNN model captures local feature patterns, while the ViT captures the global relationships within the image, by combining these architectures can provide highly effective method for facial recognition tasks. the proposed model use both CNN and ViT in feature extraction and provides robustness. From the experiment results is observed that the proposed model achieves an accuracy of 98%, outperforming conventional methods in the domain of online examination security. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on image Tibetan Caption Generation Method Fusion Attention Mechanism 4

Research on Image Tibetan Caption Generation Method Fusion A...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Xia, Jianjun Yang, Xin Ni, Qiong Gao, Dingguo Tibetan Information Technology Innovative Talent Cultivation Demonstration Base Tibet University School of Information Science and Technology Lhasa China

ISBN: (纸本)9798350324303

image caption generation combines computer vision and natural language processing to generate natural language descriptions of images. With the rapid development of deep learning, the performance of end-to-end image caption generation research based on Chinese and English datasets has been improved. However, there is a lack of Tibetan caption data samples for low-resource languages, and the research on image Tibetan caption generation is almost blank. therefore, in order to promote the research and development of multilingual image generation and improve the accuracy of Tibetan image caption, VGG19, InceptionV3 and ResNet101 are used as the backbone network, and group convolution is used to replace the fully connected operation of the attention mechanism. A CNN-based An image Tibetan caption generation method that integrates with the LSTM encoding and decoding network to improve the attention mechanism. Experiments on the Flickr8K and Flickr30K-tic Tibetan caption datasets show that this method is superior to the traditional attention mechanism image caption generation method, and has achieved better performance improvement in BLEU and ROUGE-L image caption evaluation indicators. © 2023 IEEE.

关键词： image enhancement

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Leveraging Temporal Context in Human Pose Estimation: A Survey 4

Leveraging Temporal Context in Human Pose Estimation: A Surv...

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4th international conference on image processing and vision Engineering, IMPROVE 2024

作者： Skorvankova, Dana Madaras, Martin Department of Applied Informatics Comenius University Bratislava Slovakia

ISBN: (纸本)9789897586934

Human pose estimation, the task of localizing skeletal joint positions from visual data, has witnessed significant progress with the advent of machine learning techniques. In this paper, we explore the landscape of deep learning-based methods for human pose estimation and investigate the impact of integrating temporal information into the computational framework. Our comparison covers the evolution from methods based on Convolutional Neural Networks (CNNs) to recurrent architectures and visual transformers. While spatial information alone provides valuable insights, we delve into the benefits of incorporating temporal information, enhancing robustness and adaptability to dynamic human movements. the surveyed methods are adapted to fit the requirements of human pose estimation task, and are evaluated on a real large scale dataset, focusing on a single-person scenario, inferring from 3D point cloud inputs. We present results and insights, showcasing the trade-offs between accuracy, memory requirements, and training time for various approaches. Furthermore, our findings demonstrate that models relying on attention mechanisms can achieve competitive outcomes in the realm of human pose estimation within a limited number of trainable parameters. the survey aims to provide a comprehensive overview of machine learning-based human pose estimation techniques, emphasizing the evolution towards temporally-aware models and identifying challenges and opportunities in this rapidly evolving field. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： computer vision

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