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Rethinking Implicit Neural Representations For vision Learners 48

Rethinking Implicit Neural Representations For Vision Learne...

引用

48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Song, Yiran Zhou, Qianyu Ma, Lizhuang Shanghai Jiao Tong University China

ISBN: (纸本)9781728163277

Implicit Neural Representations (INRs) are powerful to parameterize continous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution, and image generation. the questions on how to explore INRs to high-level tasks and deep networks are still under-explored. Existing INRs methods suffer from two problems: 1) narrow theoretical definitions of INRs are inapplicable to high-level tasks;2) lack of representation capabilities to deep networks. Motivated by above facts, we reformulate the definitions of INRs from a novel perspective, and propose an innovative Implicit Neural Representation Network (INRN), which is the first study of INRs to tackle both low-level and high-level tasks. Specifically, we present three key designs for basic blocks in INRN along with two different stacking ways and corresponding loss functions. Extensive experiments with analysis on both low-level task (image fitting) and high-level vision tasks (image classification, object detection, instance segmentation) demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： computer vision Implicit Neural Representations pattern recognition Representation Learning

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Risk early warning model of tunnel engineering based on computer vision and CNN 4

Risk early warning model of tunnel engineering based on comp...

引用

2024 4th international conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Zhong, Ming China Merchants Chongqing Highway Engineering Testing Center CO. LTD Chongqing China

Safety management in tunnel construction is important to ensure the safety of construction personnel and equipment. this paper proposes a tunnel engineering risk early warning model based on computer vision and convolutional neural network (CNN), aiming at realizing real-time monitoring and risk early warning of the construction site using modern image processing technology and deep learning algorithm. Many image data of tunnel engineering construction sites were collected, and the data were preprocessed and marked. then, the paper designs a depth CNN model for feature extraction and risk identification of construction site images. through the performance evaluation of the model on the training set and test set, the effectiveness and feasibility of the model in risk early warning are verified. the experimental results show that the proposed model achieves high accuracy, recall, and F1 score on the test set, which is superior to the traditional method based on image features and artificial rules. At the same time, we analyzed the prediction results of the model on the actual construction site images. We found that the model can effectively identify different risk factors and has strong generalization ability and practicability. To sum up, this study provides a new technical means and solution for the safety management of tunnel engineering, which has important theoretical and practical significance. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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Sentiment Analysis to Review Products based on Machine Learning 4

Sentiment Analysis to Review Products based on Machine Learn...

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4th international conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Saha, Sumit Sett, Aishik Karmakar, Samit Institute of Engineering and Management Dept. of Electronics & Communication Engineering Kolkata India University of Kalyani Dept. of Computer Science and Engineering Kolkata India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In this study, a method is proposed to represent the corresponding viewpoints and features in the form of graphs for observing the sentiment of the subscribers based on product review. the aim is to observe and empathize the situations based on real time with sentimental analysis using deep learning and machine learning. Nowadays machine learning and deep learning is applied for the solutions of the problems in computer vision, voice recognition, image processing and convolutional neural network. In this work, a convolutional neural network model has been proposed which attains a better outcome than other methods. © 2022 IEEE.

关键词： Sentiment analysis

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Human Action recognition Using Mediapipe Holistic Keypoints: A Deep Learning Approach 4th

Human Action Recognition Using Mediapipe Holistic Keypoints:...

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4th international conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Shandilya, Utkarsh Sharma, Vijeta Mishra, Deepti Cent Univ Haryana Dept Comp Sci & Engn Jaat Haryana India NTNU Norwegian Univ Sci & Technol Educ Technol Lab Dept Comp Sci IDI N-2815 Gjovik Norway

ISBN: (纸本)9783031837951;9783031837968

Human action recognition (HAR) is a crucial field in computer vision with applications ranging from video surveillance to human-computer interaction. this study explores an efficient framework for HAR by leveraging human keypoint extraction using Google's Mediapipe and Long Short-Term Memory (LSTM) networks. the Mediapipe framework provides accurate and real-time human keypoint extraction, significantly reducing the computational complexity associated with raw video data processing. these keypoints, representing skeletal movements, are utilised as input to LSTM networks, which capture the temporal dependencies vital for action classification. the proposed method is evaluated on two benchmark datasets: UCF101 and Kinetics 400. UCF101 contains 13,320 video clips across 101 action classes, while Kinetics 400 features 400 human action categories. the combination of Mediapipe for feature extraction and LSTM for temporal modelling achieves 92.40% and 86.8% accuracy on UCF101 and Kinetics400, respectively. the results demonstrate the effectiveness of keypoint-based approaches for HAR. this study highlights the potential of lightweight, real-time human action recognition frameworks suitable for resource-constrained environments, especially for edgeAI and edge Robotics.

关键词： Human action recognition human-computer interaction keypoint extraction LSTM Mediapipe

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Heterogeneous Acceleration System for Scene Text recognition 4

Heterogeneous Acceleration System for Scene Text Recognition

引用

4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Feng, Hua Wu, Junmin Department of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China

ISBN: (纸本)9798400708831

Scene text recognition has a wide range of applications. Its deep learning algorithm model is mainly CNN and RNN hybrid neural network, which can effectively identify text information in natural scenes. However, complex network models lead to longer inference time. this paper proposes a heterogeneous acceleration system for the scene text recognition neural network model CRNN. Mainly performed hardware deployment and optimization on CNN and LSTM. For CNN, blocking computation and a new data access pattern are proposed to reuse data and reduce on-chip storage. For LSTM, it is implemented in modules, and a method to overcome data dependency is proposed to improve processing efficiency. We implemented this accelerator on the Xilinx ZCU104 platform, which has good performance and low resource utilization. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Navigating the Future: Advancing Autonomous Vehicles through Robust Target recognition and Real-Time Avoidance 4

Navigating the Future: Advancing Autonomous Vehicles through...

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4th international conference on Control theory and Applications, ICoCTA 2024

作者： Hussein, Mohammed A. M. Habib, Maki K. The American University in Cairo Robotics Control and Smart Systems Cairo Egypt The American University in Cairo Department of Mechanical Engineering Cairo Egypt

ISBN: (纸本)9798331506155

this research addresses the critical challenge of enhancing object recognition and real-Time response capabilities in autonomous vehicles (AVs) under varying simulated conditions, which is crucial for ensuring both navigational safety and operational efficiency. Utilizing the Carla 0.9.14 simulator and Unreal Engine 4.26 on Ubuntu 20.04, we focus on improving the detection and classification of key on-road obstacles-vehicles, pedestrians, and cyclists-using the YOLOv7 object detection algorithm. By integrating advanced sensory technologies, specifically stereo vision cameras and LIDAR, we create a dynamic testing environment that simulates diverse urban and rural scenarios. Our methodology enhances the YOLOv7 algorithm's accuracy and speed through extensive training on a meticulously curated dataset of 4,113 images, reflecting a broad spectrum of environmental conditions, including varying lighting and weather conditions. this rigorous approach yielded a significant increase in mean average precision (mAP) from 64.3% to 76.3%, and enhanced the algorithm's reliability, with notable improvements over previous models. the research delineates a clear advancement in AV technology by demonstrating substantial improvements in object detection metrics, contributing foundational insights for future implementations in real-world settings and supporting the further development of real-Time avoidance systems. this study not only progresses the field of AV but also sets a new benchmark for object detection performance, aligning with industry and academic goals to optimize AV systems for complex and unpredictable driving scenarios. © 2024 IEEE.

关键词： Stereo image processing

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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

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Integration of vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle's Departure and Arrival Times 23

Integration of Vision IoT, AI-based OCR and Blockchain Ledge...

引用

8th international conference on Machine Learning Technologies (ICMLT)

作者： Sichinga, Marumbo Nizeyimana, Eric Gatare, Ignace Nsenga, Jimmy Univ Rwanda African Ctr Excellence Internet Things ACEIoT Kigali Rwanda

ISBN: (纸本)9781450398329

Vehicle based logistics are hinged on their ability to timely deliver goods, services, and people. the classical expression of "time is money" comes alive in the logistics industry yielding potentially huge financial and health consequences in case of missing deadlines. this is especially the case for time sensitive pharmaceuticals, delivery of perishable goods, delivery of people travelling, delivery of services in fault fixing/recovery sector. All these use cases motivate the need for an immutable, secure, and immortalized process of tracking time. To solve this challenge, this paper presents prototype-based research that integrates the 4th industrial revolution technologies of vision Internet of things (IoT), Artificial Intelligence (AI)-based Optical Character recognition (OCR) and blockchain. the developed prototype features a Raspberry-PI board embedding a camera, an Artificial Intelligence (AI) model to recognize plate letters from the image and a crypto wallet to sign the logging of plate number and time events on the NEAR blockchain, an emerging sharded, proof-of-stake, layer-one blockchain that is simple to use, secure and scalable. the effective operation of the developed prototype has been validated inside a campus parking and shows an accuracy of 80%. the benefits of transparency, security, and immutability of the blockchain combined with the intelligence, data capture, and processing of IoT will enable to develop accountability solutions trusted by all different logistic stakeholders.

关键词： Web3 Blockchain Dapp NEAR IoT YoloV4 OCR

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the image Segmentation of Osmanthus fragrans Based on Optimization Algorithms 4

The Image Segmentation of Osmanthus fragrans Based on Optimi...

引用

4th international conference on Advances in computer Technology, Information Science and Communications, CTISC 2022

作者： Xiao, Han Cheng, Yuzhu Suzhou University School of Electronic and Information Engineering Suzhou China Nanjing Forestry University College of Mechanical and Electrical Engineering Nanjing China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

image segmentation is of great importance in many areas including artificial intelligence, image processing, computer vision, etc. the optimization of image segmentation can be converted to seek the best threshold values to divide an image into specific objects and background. In this study, we have used the newly proposed AO, G-NHGWO, GBO, LPB and LA optimization algorithms to identify the optimal threshold values for image segmentation of Osmanthus fragrans flowers. the widely used Kapur, Shanbhag, weighted Otsu, Kittler, Yen and Li methods were utilized as objective functions for thresholding selection. the image segmentation results have exhibited the ability and superiority of employed optimization algorithms and thresholding strategies. the research of efficient image segmentation of Osmanthus fragrans in this study is helpful to the machine recognition and classification of the widely cultivated plants, which will improve the automatic flower picking and industrialization of Osmanthus fragrans. © 2022 IEEE.

关键词： image segmentation

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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...

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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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