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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

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Design of Multi view Natural Language Query System on Machine vision 4

Design of Multi View Natural Language Query System on Machin...

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4th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Fang, Quanrong Investment Co. Ltd Beijing China

ISBN: (纸本)9798350359589

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. This article first discusses the system architecture of four modules: data input and preprocessing, visual feature extraction, natural language understanding and matching, and result generation and feedback. Then, the application of machine vision technology in the system was analyzed using two calculation formulas: grayscale conversion and binarization, and natural language processing technology was briefly discussed. Subsequently, a context understanding module was added to construct a multi view natural language query system based on machine vision. Finally, two sets of simulation experiments were conducted to draw the following conclusion: compared with traditional methods, the overall average improvement in image recognition accuracy indicators is about 14.3%, while the overall average improvement in response speed indicators is about 26.5%. This research system can effectively process images from different perspectives and match them with natural language queries. © 2024 IEEE.

关键词： machine vision technology multi view processing natural language query system design

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5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

引用

5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning. The topics include: Digital Twin Technology Approach Based on the Hierarchical IDP Tensor Decomposition;modeling Technology for Complex Dynamic Operating Environment of Power Grid Based on Digital Twins;design of a Digital Twin Platform Based on Distributed Computing and Resource Optimization algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional Signal processing;time Series Prediction Application of Deep Learning in Multidimensional Signal processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D images Through Integrated Deep Learning and Natural Language processing Techniques;children’s Toy Product Design Based on Augmented Reality Technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, Based on image Features;visualization and Analysis of CNN Adversarial Training;video violence Detection Method Based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery Technology Based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text Recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models;a Short video Retrieval Method Based on Spatio-Temporal Feature Fusion;spatial Data Transformation and Fusion Expression Based on Geometric Algebra;EM2-YOLO: Lightweight Remote Sensing image Detection;Tower-Type Detection of UAV Aerial image Based on YOLOV5 Network Model.

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Health Technology Assessment-Based Evaluation Study of a Surgical Imaging System with a Built-in LED-based Intelligent Temperature Balance Control Algorithm 2

Health Technology Assessment-Based Evaluation Study of a Sur...

引用

2nd International conference on Biomedical and Intelligent systems, IC-BIS 2023

作者： Fang, Jianfei Keio University Department of Mechanical Engineering 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan

ISBN: (纸本)9781510666733

Currently, intelligent algorithms are widely used in the field of image processing to reduce the impact of fog on images. Surgical imaging systems are medical imaging devices used in surgical operations. With the continuous improvement of medical imaging technology and the application of advanced intelligent algorithms in medical devices, laparoscopic surgery is increasingly replacing traditional open surgery. However, due to the difference in temperature inside and outside the abdomen, lens fogging during laparoscopic surgery can affect the surgical field of view and interfere with the surgical process. The intelligent temperature balance control system, as a new technology, aims to improve the imaging field of view at different temperatures by combining hardware design with intelligent imaging algorithms. However, there is currently no evidence in the literature to support the anti-fog effect of surgical imaging systems equipped with this function. Therefore, this study aims to evaluate the advantages of surgical imaging systems equipped with the intelligent temperature balance control algorithm in clinical applications through comparative testing experiments and the use of Health Technology Assessment (HTA) tools and methods. © 2023 SPIE.

关键词： Imaging systems

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The Development of Intelligent VR systems Based on Deep Learning 2nd

The Development of Intelligent VR Systems Based on Deep Lear...

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2nd World conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： An, Yaohui Wang, Xinkai Wu, Wei Lei, Jiayu School of Aeronautics and Astronautics Shenyang Aerospace University Liaoning China School of Energy and Environment Shenyang Aerospace University Liaoning China

ISBN: (纸本)9789819970100

By applying deep learning technology to smart virtual Reality (VR) devices, it can achieve continuous iterative upgrades and provide users with a better immersive design experience. Based on deep learning technology, this research builds an image difference prediction calculation model and creates prediction models for pictures using data from visual interaction screens after analysis and processing. For immersive display design screens, an image prediction computational model is first built. In the experiment, numerous screenshots of the display device are processed, superfluous pixels are removed, and multiple colour display judgements are made using the image difference prediction model. The quality level of the display effect has successfully been raised based on the data from the initial colour sample points. Additionally, the enhanced display screen allows for specular reflection to arise from real-time algorithms rather than just simple mirror mapping;further enhancing the visual effect. This has great significance for the application of VR technology in education, entertainment and other fields. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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An Improved Neural Classification Logic to Predict Kidney Disease Identification Using Medical image processing Principles

An Improved Neural Classification Logic to Predict Kidney Di...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Balaji, C.G. Balachandran, G. Imran, Md. Ujwala, S.Vamsi Edlabadkar, Ajinkya P. Fawareh, Hamed Department of Digital and Telecom Management Lavale Maharashtra Pune412115 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam Andhra Pradesh PrakasamDistrict523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Kidney disease is a significant global health concern, requiring accurate and early diagnosis to prevent progression and ensure timely treatment. This study proposes an improved neural classification logic combining an AutoEncoder and an Artificial Neural Network (ANN) to predict kidney disease using medical image processing techniques. A dataset comprising kidney images from CT, MRI, and ultrasound was utilized to train and test the model. The AutoEncoder was employed for unsupervised feature extraction, reducing dimensionality while retaining key information related to kidney abnormalities. The ANN then classified these features into healthy or diseased categories. image preprocessing techniques such as noise reduction, resizing, normalization, and contrast enhancement were applied to ensure data consistency. Additionally, advanced segmentation algorithms like U-Net were used to isolate the kidney regions from the background, focusing on the Regions of Interest (ROI). Data augmentation was applied to improve generalization. The proposed model achieved a classification accuracy of 94.8%, outperforming baseline models such as CNN (92.1 %) and SVM (88.7%). The AutoEncoder + ANN combination demonstrated superior precision, recall, and Fl-score, with an AUC-ROC of 97.4%. The results highlight the model's robustness and potential for practical applications in the early detection of kidney disease. © 2024 IEEE.

关键词： Noise abatement

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image Enhancement by Various Segmentation Techniques Using Machine Learning 6th

Image Enhancement by Various Segmentation Techniques Using M...

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6th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2021

作者： Chanchlani, Akshita Thakare, vilas M. Wadhai, vijay M. Department of Computer Science and Engineering SGBA University Amravati India Department of Electronics and Telecommunications Engineering D. Y. Patil College of Engineering Akurdi Pune India

ISBN: (纸本)9789811935701

Brain Cancer is a disease where abnormal cells grow without control and can invade other tissues, if it is identified at the early stage and treated appropriately then it can save human life. In developing countries cause of disease like brain cancer is effecting many people. Brain Cancer Medical image processing is most important and challenging field now a days. image preprocessing and its segmentation on brain cancer Magnetic Resonance Imaging (MRI) and its analysis is process of finding the objects and understanding their significance. image segmentation have become most popular due to its usage into various applications. The objective of this paper is exploring and implementation of various algorithms of image segmentation on medical image specifically on brain cancer MRI. image segmentation is very important step of understanding the images for further processing and analysis. This paper represents implementation of various image segmentation techniques that includes image Thresholding, Applying Various Morphological Operations, edge and region-based segmentation for enhancement of brain cancer images and detection of contours from brain cancer MRI. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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Integration of Cryptography and Navigation systems in Unmanned Military Mobile Robots: A Review of Current Trends and Perspectives 8

Integration of Cryptography and Navigation Systems in Unmann...

引用

8th International conference on Digital Technologies in Education, Science and Industry, DTESI 2023

作者： Bakyt, Makhabbat Moldamurat, Khuralay Konyrkhanova, Assem Maidanov, Adil Satybaldina, Dina L.N. Gumilyov Eurasian National University Satpayev 2 Astana010008 Kazakhstan

This paper addresses the intersection of military robotics, secure data transmission, and reliable navigation systems. The navigation system is tailored for maze traversal, allowing an operator to set start and end points through Matlab functions. Navigational data, obtained from a camera situated above the terrain, is transmitted to a PC via S-video input, initiating a Matlab-based navigation algorithm. The study emphasizes cybersecurity and precise navigation, incorporating cryptographic methods in LoRa communication devices and implementing quantum-resistant algorithms in secure robot operating systems. An image processing algorithm facilitates route planning within the maze, generating a comprehensive overview of contemporary techniques. visual representations of the wireless robot navigation system and maze encryption algorithm are included for clarity. © 2023 Copyright for this paper by its authors.

关键词： Navigation systems

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Level Set Method Based on Molecular Beam Epitaxy Equation for image Segmentation 8th

Level Set Method Based on Molecular Beam Epitaxy Equation f...

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8th International Scientific conference on Intelligent Information Technologies for Industry, IITI 2024

作者： Kuang, Di Guo, Zhichang Song, Fanghui School of Mathematics Harbin Institute of Technology Heilongjiang Harbin150001 China

ISBN: (纸本)9783031774102

image segmentation is an important and complex task in computer vision. The variational level set method has become a popular approach for image segmentation due to its topological invariance. However, the evolution process may be unstable, leading to segmentation failures due to excessively flat or sharp final evolutionary profiles. In order to improve the accuracy and stability of evolution, we propose a high-order level set variational model, which applies the variational form of the molecular beam epitaxy (MBE) equation. The model avoids the need for re-initialization. In order to calculate the model, we drive the gradient flow of the model. We propose a finite difference semi-implicit and semi-explicit scheme. Additionally, we also design the scalar auxiliary variable scheme of the model, which expands its application, and can be solved by fast algorithms. Numerical experiments show that the model is outperforms current mainstream models in the processing of noisy images and medical images, and can be applied to actual image segmentation problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Molecular beam epitaxy

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Non Homogeneous Realistic Single image Dehazing

Non Homogeneous Realistic Single Image Dehazing

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23rd IEEE/CVF Winter conference on Applications of Computer vision (WACV)

作者： vinay, P. Abhisheka, K. S. Shetty, Lithesh Kushal, T. M. Shylaja, S. S. PES Univ Bangalore Karnataka India

ISBN: (纸本)9798350320565

A hazy image is one where atmospheric effects degrade the contrast and visibility of the image. It is often caused by the dispersion of light into the moisture particles present, smoke etc. This results in lower performance in high level vision tasks such as object detection, free space detection, scene understanding, etc. Hence the images have to be dehazed before applying other high level algorithms. Dehazing is the process of reconstructing the original colour and contrast of the image if taken in normal conditions. image dehazing is a non-trivial task as it is hard to collect haze free ground truth images. Further, achieving dehazed images when variable haze is present is a significantly harder challenge. In this research, we propose the Non Homogeneous RESIDE dataset (NH-RESIDE) that contains images created synthetically using the principles of randomness and representativeness. Experimental results show that the model trained on our dataset produces visually more pleasing images with a much better dehazing effect on real world images. The model implemented in this paper also outperforms the state-of-the-art models by a huge margin on the NH-Haze dataset proposed by the NTIRE Non Homogeneous Dehazing Challenge at CVPR, achieving an average PSNR of 25.69 and an average SSIM of 0.80. It also achieves much better processing times when compared to other models, thereby facilitating real-time performance.

关键词： Computer vision image color analysis conferences Moisture Object detection Computer architecture Real-time systems

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YOLOv9 in Action: A Futuristic Approach to Real-Time License Plate Recognition and Analysis

YOLOv9 in Action: A Futuristic Approach to Real-Time License...

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2024 IEEE International conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Kumar, M Ranjith Sendur, G Jeevan Kumar, S Praveen Annamalai, R. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

The automated recognition and identification of license plates is an essential element of intelligent transportation systems that enable effective traffic management, security measures, and the development of efficient e-payment algorithms in toll booths and parking facilities. Even though license plate recognition (LPR) technology is fairly advanced, significant efforts are being made to enhance its accuracy and resilience The growing adoption of deep learning technologies across multiple industry domains, particularly computer vision, presents a novel idea implemented in this paper. To develop an approach to improve detection accuracy using YOLOv9 and transfer learning. Through combining the power of YOLOv9 with transfer learning, it is feasible to tackle the traditional LPR system's inherent flaws. Character recognition, picture pre-processing, license plate extraction, and character segmentation are the four main parts of the proposed system. Each step involves unique methodologies, primarily focusing on character recognition methodologies using YOLOv9 which has not been adequately tackled in existing research. By thorough testing and analysis, this study seeks to further the current developments in LPR systems, ensuring discernibly better-established intelligent transportation solutions. We have achieved a mAP50 Score of 99.5% and a mAP50-95 score of 71 % © 2024 IEEE.

关键词： image segmentation

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