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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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image Style Transfer Based on VGG Neural Network Model

Image Style Transfer Based on VGG Neural Network Model

引用

2022 IEEE International conference on Advances in Electrical Engineering and Computer applications, AEECA 2022

作者： Tao, Yilin Beijing Language and Culture University Beijing China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

image style transfer is an important research content related to image processing in computer vision. Compared with traditional artificial computing methods, deep learning-based convolutional neural networks in the field of machine learning have powerful advantages. This new method has high computational efficiency and a good style transfer effect. To further improve the quality and efficiency of image style transfer, the pre-trained VGG-16 neural network model and VGG-19 neural network model are used to achieve image style transfer, and the transferred images generated by the two neural networks are compared. The research results show that the use of the VGG-16 convolutional neural network to achieve image style transfer is better and more efficient. © 2022 IEEE.

关键词： Neural network models

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A Hybrid Density-Based Clustering Pipeline for Track Reconstruction

A Hybrid Density-Based Clustering Pipeline for Track Reconst...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Bijia You Zhiyun Xia School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In high-energy physics, the capability to accurately and efficiently track charged particles is essential for effective data analysis. This article introduces an innovative density-based clustering pipeline intended for the track reconstruction task, incorporating Density-Based Spatial Clustering of applications with Noise (DBSCAN) algorithm and Ordering Points To Identify the Clustering Structure (OPTICS) algorithm. Results on simulated data suggest that the proposed method offers improvements in both effectiveness and robustness compared to traditional techniques, with performance on par with state-of-the-art neural network-based approaches. Furthermore, this pipeline demonstrates significant potential for real-time applications in high-energy physics experiments, offering a scalable and robust solution.

关键词： machine learning algorithms Pipelines Noise Clustering algorithms Optics Robustness Real-time systems Pattern recognition Trajectory image reconstruction

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Real Time Object Detection and Clasification Using Small and Similar Figures in image processing 3

Real Time Object Detection and Clasification Using Small and...

引用

3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Thakur, Kanika Banerjee, Pallab Naaz, Farheen Banerjee, Probal Amity School of Engg. and Technology Amity University Jharkhand Ranchi India Govt. Polytechnic Jharkhand Ranchi India

ISBN: (纸本)9798350302288

In the present time, there has been many adaptations of Object Detection is developed. Object Detection means catching the object name and it's other characteristics in an image or a video. This field is known to be the most difficult technique in computer background. In this very paper, a new and simplest way of detection images and objects is shown using the machine Learning approach. Two methodologies is adapted in this project. First is Open cv algorithm which is a library of machine learning software and open to all developers. It provide an infrastructure that is common for all computer applications which helps to accelerate machine learning commercial works and products. Second is Yolo algorithm short for You Only Look Once. This algorithm is used for the prediction of objects and images which defines the point location of that object in dimensional way. In this, the technique of object detection is applied to like figures which are small and similar to one another. © 2023 IEEE.

关键词： Computer vision

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Remote Sensing image Captioning (RSIC): A Technical Review 1st

Remote Sensing Image Captioning (RSIC): A Technical Review

引用

1st International conference on Data Engineering and machine Intelligence, ICDEMI 2023

作者： Dhinesh, A. Sumathy, P. Department of Computer Science Bharathidasan University Tamilnadu Tiruchirappalli620023 India

ISBN: (纸本)9789819776153

Remote Sensing image Captioning (RSIC) is crucial for many researchers since it has many applications in environmental monitoring, disaster management, urban planning, image retrieval, performance of building planes, military intelligence, and autonomous vehicles. The effective procedure to generate the captions from remote sensing images complements the above-mentioned application domains. Various baseline data sets have been created by the researchers to enhance the quality of captioning by processing the diverse features of the geospatial information. In this paper, we have technically reviewed important literature that follow different algorithms for generating the captions. For example, we have presented the technical review on vision-Language Aligning Paradigm (VLCA) under the bi-lingual caption generation model, Joint-Training Two-Stage (JTTS) technique under multimodel fusion category, Multilevel and Contextual Attention Network (MLCA-Net) under context-aware captioning, LEviR-CC belongs to transfer learning model, BERT and GPT-3 models belong to transfer-based model, Multiscale Attention (MSA) and Multifeat Attention (MFA) of Multiscale captioning model and Summarization Driven (SD)-RSIC of fine-grained captioning model. We have also presented the performance of each of these methods on various benchmark datasets. For evaluation, different well-known performance metrics are considered. The result is critically evaluated and commented on. In the future, a more rigorous review of these methods along with other relevant methods will be presented along with implementation data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Urban planning

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A Novel Resource-Constrained Insect Monitoring System based on machine vision with Edge AI 5

A Novel Resource-Constrained Insect Monitoring System based ...

引用

5th IEEE International conference on image processing applications and Systems (IPAS)

作者： Kargar, Amin Wilk, Mariusz P. Zorbas, Dimitrios Gaffney, Michael T. O'Flynn, Brendan Univ Coll Cork Tyndall Natl Inst Cork Ireland Nazarbayev Univ Dept Comp Sci Nur Sultan Kazakhstan Teagasc Ashtown Food Res Ctr Hort Dev Dept Dublin Ireland

ISBN: (纸本)9781665462198

Effective insect pest monitoring is a vital component of Integrated Pest Management (IPM) strategies. It helps to support crop productivity while minimising the need for plant protection products. In recent years, many researchers have considered the integration of intelligence into such systems in the context of the Smart Agriculture research agenda. This paper describes the development of a smart pest monitoring system, developed in accordance with specific requirements associated with the agricultural sector. The proposed system is a low-cost smart insect trap, for use in orchards, that detects specific insect species that are detrimental to fruit quality. The system helps to identify the invasive insect, Brown Marmorated Stink Bug (BMSB) or Halyomorpha halys (HH) using a Microcontroller Unit-based edge device comprising of an Internet of Things enabled, resource-constrained image acquisition and processing system. It is used to execute our proposed lightweight image analysis algorithm and Convolutional Neural Network (CNN) model for insect detection and classification, respectively. The prototype device is currently deployed in an orchard in Italy. The preliminary experimental results show over 70 percent of accuracy in BMSB classification on our custom-built dataset, demonstrating the proposed system feasibility and effectiveness in monitoring this invasive insect species.

关键词： machine vision image processing Deep Learning Edge AI Integrated Pest Monitoring Food Security

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Road Infrastructure Defect Detection using Yolo8Seg Based Approach

Road Infrastructure Defect Detection using Yolo8Seg Based Ap...

引用

International image processing, applications and Systems conference (IPAS)

作者： Norah A. AlSubaie Ghayda A. AlMalki Ghada N. AlMutairi Sarah A. AlRumaih Department of Computer Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This research introduces "Jaddah," an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency.

关键词： Measurement Location awareness Computer vision Urban planning machine learning Inspection Road safety Maintenance Mobile applications Defect detection

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Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing 12

Deep Ensemble Learning with Frame Skipping for Face Anti-Spo...

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12th International conference on image processing Theory, Tools and applications, IPTA 2023

作者： Muhammad, Usman Hoque, Md Ziaul Oussalah, Mourad Laaksonen, Jorma University of Oulu Center for Machine Vision and Signal Analysis Finland Aalto University Department of Computer Science Finland

ISBN: (纸本)9798350325416

Face presentation attacks, also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario. © 2023 IEEE.

关键词： Face recognition

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714696

The proceedings contain 108 papers. The special focus in this conference is on Wireless Artificial Intelligent Computing Systems and applications. The topics include: Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline;truthful Double Auction-Based Resource Allocation Mechanisms for Latency-Sensitive applications in Edge Clouds;V2ICooper: Toward Vehicle-to-Infrastructure Cooperative Perception with Spatiotemporal Asynchronous Fusion;recommendation-Aware Collaborative Edge Caching Strategy in the Internet of Vehicles;towards Robust Internet of Vehicles Security: An Edge Node-Based machine Learning Framework for Attack Classification;device-Edge-Cloud Collaborative video Stream processing and Scheduling Strategy Based on Deep Reinforcement Learning;consistent Low-Latency Scheduling for Microsecond-Scale Tasks in Data Centers;multi-scale Data Reconstruction Based Policy Optimization Algorithm for Skill Learning;Joint Optimization Design of Intelligence Reflecting Surface Assisted MU-MISO System Based on Deep Reinforcement Learning;Secure Motion Verification for High Altitude Platforms with a Hybrid AOA-TDOA-FDOA Scheme;variational Autoencoder Based Automatic Clustering for Multivariate Time Series Anomaly Detection;deep Reinforcement Learning Based Economic Dispatch with Cost Constraint in Cyber Physical Energy System;inceptionNeXt Network with Relative Position Information for Microexpression Recognition;adMarks: image Steganography Based on Adversarial Perturbation;a Review on Binary Code Analysis Datasets;low-Cost Robot Path Planning Mechanism for Escaping from Dead Ends;towards Communication-Efficient Collaborative Perception: Harnessing Channel-Spatial Attention and Knowledge Distillation;improving Anomaly Scene Recognition with Large vision-Language Models;ReSU-Net: State Space Model for 3D Abdominal Multi-organ Segmentation;autocue : Targeted Textual Adversarial Attacks with Adversarial Prompts;universal Sign Language

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

18th International Conference on Wireless Artificial Intelli...

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18th International conference on Wireless Artificial Intelligent Computing Systems and applications, WASA 2024

ISBN: (纸本)9783031714634

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Advanced Techniques for Chinese image Captioning: Investigating Attention Mechanisms Based on Object Detection for Chinese image Caption Generation

Advanced Techniques for Chinese Image Captioning: Investigat...

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machine Learning and Computer Application (ICMLCA), International conference on

作者： Yongbin Hua Pei'ang Li Xiangjin Zeng Hong Xu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds significant theoretical and practical value. Inspired by top-down attention mechanisms, this paper proposes an innovative attention model. Utilizing the output of pretrained object detection networks as prior knowledge for images, the model guides the generation of natural language descriptions. By directly incorporating the results of object detection as attention inputs into the text generation network, the model effectively focuses on key descriptive regions of images, thereby significantly enhancing performance. On public Chinese image captioning datasets, this model demonstrates substantial advantages in metrics such as BLEU-4 and METEOR.

关键词： Measurement Knowledge engineering Attention mechanisms Computational modeling machine vision Object detection Computer applications Data models Natural language processing Meteors

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