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

共 5172 条记录，以下是411-420 订阅

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BARTPhoBEiT: Pre-trained Sequence-to-Sequence and image Transformers Models for Vietnamese Visual Question Answering 6

BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Tran...

引用

6th international conference on Multimedia Analysis and pattern recognition, MAPR 2023

作者： Tran, Khiem Vinh Nguyen, Kiet Van Nguyen, Ngan Luu thuy University of Information Technology Ho Chi Minh city Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

Visual Question Answering (VQA) is an intricate and demanding task that integrates natural language processing (NLP) and computer vision (CV), capturing the interest of researchers. the English language, renowned for its wealth of resources, has witnessed notable advancements in both datasets and models designed for VQA. However, there is a lack of models that target specific countries such as Vietnam. To address this limitation, we introduce a transformer-based Vietnamese model named BARTPhoBEiT. this model includes pre-trained Sequence-to-Sequence and bidirectional encoder representation from image Transformers in Vietnamese and evaluates Vietnamese VQA datasets. Experimental results demonstrate that our proposed model outperforms the strong baseline and improves the state-of-the-art in six metrics: Accuracy, Precision, Recall, Fl-score, WUPS 0.0, and WUPS 0.9. © 2023 IEEE.

关键词： Visual languages

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Detecting Fruit Diseases Using Deep Learning and image Analysis 4

Detecting Fruit Diseases Using Deep Learning and Image Analy...

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Pareek, Tushar P Dobariya, Deep Mahajan, Tanay Bhise, Dhiraj SVKM's NMIMS MPSTME Department of Information Technology Shirpur India

ISBN: (纸本)9798350306118

the implementation of several agriculture-related issues was made simple by advancements in computer vision technology. the detection of fruit diseases is one such issue. Using deep learning techniques, a lot of study has been done on many fruits, including apples, mangos, kiwis, passion fruit, and others. the most significant contributions made by this field in recent years are outlined in this review paper. In this review, we have performed a technical analysis of deep learning methods for predicting fruit illnesses. Along with the deep learning models utilized, the study also compares various picture acquisition, image pre-processing, and segmentation methods. the study discovered that the most accurate deep learning model can vary based on the system's computing capacity and the data used. In the article directions for future research have also been covered. © 2023 IEEE.

关键词： Fruits

来源：评论

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A visual place recognition method based on semantic instance and bag context 4

A visual place recognition method based on semantic instance...

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： Wang, Ruitang Ma, Changshe Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Guangzhou China School of Computer Science South China Normal University Guangzhou China

ISBN: (纸本)9781510672505

Visual place recognition is an essential function for robot system, which can effectively reduce position error via traversing same place. However, a major challenge in this field is to be robust against viewpoint and illumination changes caused by environmental variations. To solve this problem, we propose a novel visual place recognition method in coarse-to-fine manner, which incorporating both high-level semantic feature and bag context information. Firstly, the semantic features of an image are obtained by adopting deep learning network, which converts the image data to the instance representation in more simple way. In coarse stage, we utilize the Bag of word (BoW) model to quantify semantic features by calculating their frequency distribution. In fine stage, we enhance the spatial correlations of semantic objects to further distinguish similar scenes. through the above two stage, we achieve a robust pipeline of visual place recognition to tackle viewpoint and condition variations. Experimental results on several datasets, including both indoor scenes and outdoor scenes, show that our method achieve good performance, especially for extreme conditions. © 2023 SPIE.

关键词： Semantics

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image Despeckling Using Hybrid Method 1

引用

4th international conference on Communications and Cyber-Physical Engineering, ICCCE 2021

作者： Saritha, M. Rasane, Krupa Jain College of Engineering Visvesvaraya Belagavi Belgaum India

ISBN: (数字)9789811679858

ISBN: (纸本)9789811679841

the Synthetic Aperture Radar (SAR) is a satellite imaging which is severely degraded by noise and appears as granular pattern which affects the overall quality of the image and causes difficulty in interpretation and further processing like segmentation, detection and recognition of the target in the image for computer vision systems and also for manual interpreters. Speckle noise is multiplicative nature and it is complicated to filter it. In this paper a hybrid methods consisting of Frost filter and bayes thresholding are implemented for the removal of Speckle noise and qualitative measures like PSNR and MSE are evaluated. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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recognition of Handwritten English Character Using Convolutional Neural Network 4

Recognition of Handwritten English Character Using Convoluti...

引用

4th international conference on Artificial Intelligence and Speech Technology, AIST 2022

作者： Katoch, Sapna Rakhra, Manik Singh, Dalwinder Lovely Professional University Department of Computer Science Phagwara India

ISBN: (纸本)9781665499026

In the domain of computer vision and image processing, one of the most active and difficult study fields is handwritten character recognition. It may be used as a reading tool for bank checks, for identifying characters on forms, and for a slew of other purposes. the optical character recognition of the papers is similar to documents produced by hand by a human. this OCR is put to use to improve the simplification of the process of character translation, which may be obtained from a broad range of file types, such as image and word document files. Researchers have made tremendous progress in HCR by making use of vast amounts of raw data and new breakthroughs in Deep Learning and Machine Learning algorithms. the fundamental purpose of this research paper is to give a solution for several techniques of handwriting recognition. these methods include the usage of touch input through a mobile screen as well as the use of an image file. CNN is used to identify characters in a test dataset in this work. Work on CNNs' capacity to detect characters from a picture dataset and their accuracy of recognition will be examined. Characters are recognized by CNN by comparing and contrasting their shapes and distinguishing characteristics. the dataset A-Z Handwritten was used to test our CNN implementation's handwriting accuracy and model gives the 100% result to recognize the character. © 2022 IEEE.

关键词： Convolutional neural networks

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Intelligent Monitoring and Management of Trade Logistics Based on Big Data and computer vision Technology 4

Intelligent Monitoring and Management of Trade Logistics Bas...

引用

4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Gao, Zhongpo Kang, Chengwen Wu, Xufeng School of Economics Harbin University of Commerce Harbin150028 China School of Economics and Management Harbin University Harbin150086 China

Technologies such as big data and computer vision are increasingly being applied in trade logistics, and their integration provides strong support for achieving modernization and intelligence in the logistics industry. this article utilizes various sensors, global positioning systems, radio frequency identification and other technologies to obtain real-time information on the location, temperature, and transportation status of goods. Video surveillance and image recognition methods can be used, combined with computer vision technology, to monitor the loading and unloading process of goods and the condition of transportation vehicles in real time. this article studies an intelligent monitoring algorithm that achieves anomaly detection, warning prompts, and ensures the safety and smoothness of transportation. Exploration of transportation path optimization in batch 1 experimental group (big data analysis) saved 3 hours of time and cost by 500 yuan. this article explores the intelligent monitoring and management of trade logistics based on big data and computer vision technology, which can help improve transportation efficiency and reduce costs. © 2024 the Authors.

关键词： Radio frequency identification (RFID)

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Research on Network Traffic Feature Map Generation and Evaluation Methods 4

Research on Network Traffic Feature Map Generation and Evalu...

引用

4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Jiang, Anxing Shi, Kai Wang, Mengshu Zhang, Zeri Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350329490

With the continuous development of machine learning, it has been widely used in the fields of image processing and computer vision. However, it needs to be transformed into images before feeding network traffic data into a deep learning model for training. the existing methods for directly converting network traffic data into images demonstrate unfavorable results. therefore, this paper proposes a method for generating network traffic feature maps. this method first uses information entropy and classical k-nearest neighbors estimation algorithm to calculate the entropy values for discrete and continuous variables respectively, then the entropy values are used to filter network traffic. Finally, feature maps are generated through an encoder. Experiments on the UNSW_NB15 and CIC-IDS2017 datasets show that the proposed method outperforms other methods in terms of metrics such as PSNR and SSIM. © 2023 IEEE.

关键词： Intrusion detection

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PFMRC: A Unified MRC Framework for Chinese Named Entity recognition via Semantic Feature Fusion 4

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

引用

4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

the landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. the integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 IEEE.

关键词： Semantics

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Real-time hatch detection during unloading of dry bulk carriers with side rolling hatchcover 4

Real-time hatch detection during unloading of dry bulk carri...

引用

4th international conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Jiang, Weidong Liu, Yong Tang, Jun Liu, Yang Southwest University of Science and Technology School of Computer Science and Technology Sichuan Mianyang China Mianyang Vocational and Technical College Sichuan Mianyang China China West Normal University Sichuan Nanchong China

ISBN: (纸本)9798350394375

Accurately positioning the hatch during the loading and unloading of bulk cargo carriers is a critical factor that impacts safety and efficiency in cargo handling processes. Current methods, which involve reconstruction followed by hatch opening detection, are time-consuming and often lead to inaccurate positioning due to variations in cargo weight and sea conditions. To overcome these limitations, we propose a hatch recognition algorithm based on point cloud generation and image contour extraction to meet the rapid detection requirements of large cargo ships. We employ histogram filtering to eliminate point cloud data originating from cargo inside the hatch during real-time scanning, thereby determining the plane where the hatch opening is situated. the point cloud is then vertically projected onto the hatch cover surface and transformed into a binary image, ensuring precise hatch positioning. Experimental validation demonstrates the reliability of this algorithm, with an error rate of less than 1.5% and a processing time of under 3 seconds. this method enables real-time guidance for loading and unloading ships, contributing to enhanced cargo handling speed and safety. Furthermore, this technology holds the potential for successful application in bulk cargo handling operations at freight terminals in the future. © 2024 IEEE.

关键词： Unloading

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Energy Consumption Optimization of Swin Transformer Based on Local Aggregation and Group-Wise Transformation 4

Energy Consumption Optimization of Swin Transformer Based on...

引用

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

作者： Liu, Yuhong Chen, Shengbo Lei, Zhou Wang, Peng School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350326444

the Transformer is a natural language processing model that uses attention mechanisms, originally introduced by Google in 2017. Due to its excellent feature extraction capabilities, researchers in the industry have explored the application of Transformer in computer vision tasks. Following the success of the vision Transformer in 2020, the Swin Transformer was introduced, which outperforms traditional convolutional neural networks on various benchmarks in the vision field. As a result, Swin Transformer has become a popular backbone for practical production applications. While Swin Transformer excels in accuracy, its large parameter size and high energy consumption pose challenges for deployment at the edge. Addressing this issue is crucial for the wide-scale adoption of Swin Transformer. Current research on energy consumption for Transformer-based models is still in its early stages. To reduce model size and energy consumption while preserving accuracy, this study focuses on model compression. Specifically, we propose a combination of group-wise transformation and local aggregation methods from traditional CNN models. By introducing local aggregation and group-wise transformation calculation methods while maintaining the original shifted window attention mechanism of Swin Transformer, the proposed approach effectively reduces the number of model parameters. Our experimental results show that the Swin-Transformer model trained with this method on an RTX-3090 graphics card can save up to 24% of energy, with an accuracy improvement of about 1%. © 2023 IEEE.

关键词： Energy utilization

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