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

共 12901 条记录，以下是451-460 订阅

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Secure image Retrieval in an Untrusted Cloud Environment 4th

Secure Image Retrieval in an Untrusted Cloud Environment

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4th International conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Prakash, Vijay Khandelwal, Kavya Gupta, Disha Garg, Lalit University of Malta Msida Malta School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science & Engineering Graphics Era Hill University Dehradun India

ISBN: (纸本)9789819994410

image processing in the scrambled area has received increasing attention for its many anticipated uses, such as providing efficient and safe solutions for protection-saving applications in untrustworthy environments. The high degree of ciphertext development caused by the existing homomorphic encryptions is one barrier to the widespread use of these algorithms. This paper considers the problem of managing images in scrambled space. We develop an image encryption scheme that limits ciphertext expansion while preserving the homomorphic characteristic using image design elements. The proposed encryption scheme first uses a probabilistic homomorphic cryptosystem to jumble the image’s pixels, then packs the complete scrambled image to conserve space. Our approach offers a significantly lower ciphertext development factor while maintaining the homomorphic characteristic than the component-wise encryption scheme. It is unnecessary to demand additional intuitive norms when using secure sign-handling tools on the compressed encoded image. We provide a rapid calculation for the encryption and pressure of the proposed image encryption plot, which expedites the computation and significantly increases the effectiveness of our strategy. The security, ciphertext extension percentage, and computing complexity are also being examined. Our validation and testing approach shows that the proposed computations are valid. The proposed algorithms for image encryption make sense as an image encryption scheme for safe picture management applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image retrieval

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Advances in Artificial Intelligence, Big Data and algorithms - Proceedings of the 3rd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2023

Advances in Artificial Intelligence, Big Data and Algorithms...

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3rd International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2023

ISBN: (纸本)9781643684444

The proceedings contain 149 papers. The topics discussed include: effects of AI on smart agriculture: a case study of digital agriculture base;image retrieval-based product identification for automatic checkout systems;application of lightweight emotion recognition model in intelligent construction site monitoring;research on the application of smart wearable in the emotional management of the elderly;artificial intelligence technology in the field of broadcasting and hosting;intelligent inspection method for photovoltaic modules based on image processing and deep learning;application of AI algorithms in power system load forecasting under the new situation;research on the intelligent operation and maintenance control system of power distribution network based on big data technology;research progress of intelligent rail transit train control system;and design and implementation of intelligent potted plant management system based on Internet of Things.

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Preserving Legacies, Pioneering Frontiers: Multi-Sensor image Fusion, from Medieval Manuscripts to UAS-based Sensing 30

Preserving Legacies, Pioneering Frontiers: Multi-Sensor Imag...

引用

conference on algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

作者： Hassanzadeh, Amirhossein Macalintal, Jose N. G. M. Messinger, David Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781510673816;9781510673809

Data fusion, which involves integrating data from multiple sources, is increasingly valuable across various fields due to its ability to enhance information quality, accuracy, and reliability. This process enables a more comprehensive understanding of complex phenomena by merging diverse datasets, providing insights that are otherwise unattainable. In the realm of remote sensing, where precise data acquisition is critical, fusion techniques have become indispensable, benefiting applications such as object detection, classification, and change detection. While much emphasis has been placed on spatial sharpening techniques in published studies, there remains a notable gap in establishing robust workflows for both lab-based and UAS-based remote sensing data fusion, particularly in the near-infrared (VNIR) and short-wave infrared (SWIR) regions. This study aims to investigate VNIR-SWIR fusion using data sourced from a medieval manuscript in a controlled laboratory environment and from UAS-based sensors in a real-world setting, addressing differences in system parameters and processing workflows. Despite challenges such as image registration issues, our analysis has yielded promising results, underscoring the importance of ongoing refinement in fusion methodologies to ensure comprehensive data interpretation and analysis across diverse datasets and environments.

关键词： data fusion visible to near infrared (VNIR) shortwave infrared (SWIR) Unamnned Aerial systems (UAS)

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Acceleration of image processing algorithms based on a Single Board Computer and FPGA co-design 11

Acceleration of image processing algorithms based on a Singl...

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11th International conference on Modern Circuits and systems Technologies (MOCAST)

作者： Kokotis, Petros Vourvoulakis, John Int Hellen Univ Dept Comp Informat & Telecommun Engn Serres Greece

ISBN: (数字)9781665467179

ISBN: (纸本)9781665467179

During recent years, various hardware platforms were developed, each one suitable for use in different kind of applications. Platforms based on FPGAs, DSPs, GPUs, Single Board Computers, microcontrollers extend processing capabilities and functionality in comparison with traditional personal computers based on a single CPU. Furthermore, co-design combines advantages from different types of processing units, rendering such architectures more attractive to researchers. In this paper, we achieve acceleration of image processing algorithms using a hardware platform based on a Raspberry Pi Single Board Computer and a custom designed FPGA HAT (Hardware Attached on Top) for RPi. The FPGA HAT consists of a Cyclone 10LP device The FPGA undertakes a computationally demanding load such as robotic vision algorithms exploiting parallelism, while the RPi can apply higher level operations such as running ROS (Robot Operating System). In order to overcome bottleneck in exchanging data between RPi and FPGA, a 16-bit parallel customized protocol was developed from scratch. The achieved transfer rate was about 50 Mbytes/sec when multi threaded software was implemented for the RPi. An image edge detector was implemented in order to verify the system performance. When only the RPi was used the processing rate was 48fps for images with resolution 512x512 pixels. RPi and FPGA co-design achieved processing rate 170fps for the same resolution images, which means an acceleration of about 350%. The proposed system was also evaluated in terms of power consumption.

关键词： fpga accelerator raspberry pi

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Performance Analysis for Trichoscopy and Trichogram Using Deep Learning and image processing—A Survey 8th

Performance Analysis for Trichoscopy and Trichogram Using De...

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8th International conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Jain, Divyanshu Masurkar, Purva Kakde, Shreyash Khot, Mohammed Siddique Waghmare, Aditya Gohil, Unnati Pawar, Rahul Patel, Dhananjay Patil, Pradeep Vidyalankar Institute of Technology Wadala Mumbai India Srihtvak Skin Hair Laser STD Leprosy Pharma and Wellness Center Baner Pune India

ISBN: (纸本)9789819994854

Hair-related diseases are pervasive and can significantly impact individuals’ confidence and emotional well-being. Accurate diagnosis of these conditions poses challenges even for experienced professionals. However, the integration of technology, particularly deep learning and artificial intelligence (AI), has been showing bright results in the field of Trichology. This paper presents a comprehensive review of the techniques and technologies developed in Trichology. We begin by delineating common hair diseases, trichoscopy image acquisition methods, and available datasets. We also examine existing frameworks and tools that facilitate the creation of trichoscopy-based algorithms, along with frequently used evaluation metrics. The techniques studied in this paper involve hair loss detection using Mask R-CNN, Kaggle network, DEX-IMDB-WIKI and DEX-ChaLearn networks, deep learning-based scalp image analysis using limited data through ResNet, ResNeXt, DenseNet, and XceptionNet, image processing using grid line selection method, machine learning using SVN and KNN, and hair and scalp disease recognition using deep learning and image processing. The different algorithms used in all the mentioned techniques are analyzed, giving us a brief knowledge of how Trichology is applied with the help of technology to benefit professionals in accurately diagnosing different hair loss parameters and diseases. This review offers an overview of recent advancements in hair disease diagnosis using trichoscopy and Trichogram, emphasizing opportunities for further enhancement in this rapidly evolving field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Enhancing Liver Cancer Detection with AI-Powered image processing 4

Enhancing Liver Cancer Detection with AI-Powered Image Proce...

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4th International conference on Sustainable Expert systems, ICSES 2024

作者： Suresh Kumar, A. Rajathi, S. Vargheese, Vineetha Sudha, M. Anju, A. Lokeshkiran, P. Department of Computer Science and Engineering Rathinam Technical Campus Tamil Nadu Coimbatore India Karpagam Institute of Technology Department of Information Technology Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Rathinam Technical Campus Tamil Nadu Coimbatore India

ISBN: (纸本)9798331540364

This project aims to identify liver tumors using deep learning techniques. The core of our approach is a sophisticated neural network trained on real CT scans of patients with liver tumors. By leveraging advanced signal-processing algorithms and deep learning models, our AI method accurately distinguishes between tumors and healthy liver tissue. Accurate identification of liver tumors on CT images is crucial for planning effective radiation therapy. CT and MRI scans provide detailed images of the internal structure of the body by analyzing tissues. Our primary objective is to develop an AI-driven neural network that can reliably identify liver tumors in these images. Given the variability in liver location among individuals, our approach employs specific techniques to ensure precision. Using U-net technology, our goal is to enhance the speed and accuracy of tumor identification, thereby improving patient access to timely and accurate diagnoses. This project underscores the significant potential of deep learning applications in health care. © 2024 IEEE.

关键词： Computerized tomography

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Effective Integration of Innovative Computer Vision Tools into Electronics Manufacturing Process 7

Effective Integration of Innovative Computer Vision Tools in...

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7th International Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Adamova, Arina A. Vlasov, Andrey I. Medvedeva, Elena A. Bauman Moscow State Technical University Department of Design and Production Technology of Electronic Equipment Moscow Russian Federation

ISBN: (纸本)9798331531836

The success and accuracy of analysis and processing of raster images of electronic assemblies entering the machine vision algorithms and models within the electronics manufacturing process depend on parameters of these images. This necessitates the development of recording modules for computer vision systems using multifunctional cameras that allow not only capturing but also the maximum possible pre-processing of images. The paper analyzes the processing of raster images using machine vision methods and formulates the key requirements of computer vision algorithms and models for input images. A structural diagram of a computer vision system is presented. In accordance with this diagram, a recording module based on a 2-megapixel camera has been developed. Physical modeling of the module operation has been carried out using the example of processing an image of an electronic assembly. The proposed software and hardware version of the recording module solves the problem of effective implementation of innovative machine vision methods in electronics manufacturing process by pre-processing images on the camera side and their visualization in order to control the success of the system reconfiguration. © 2025 IEEE.

关键词： camera computer vision electronics manufacturing raster image preprocessing recording module

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Organelles segmentation in scanning electron microscopy images based on image processing 24

Organelles segmentation in scanning electron microscopy imag...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International conference on Education Digitalization and Computer Science, EDCS 2024

作者： Liu, Qiuhan Zhang, Qiang Zheng, Zhaohui Changjiang Institute of Technology Wuhan430212 China Wuhan Railway Vocational College of Technology Wuhan430200 China Wuhan Institute of Technology Wuhan430079 China

ISBN: (纸本)9798400710360

Organelles are important structures and functional systems within cells, which are of great significance for understanding life phenomena and disease mechanisms. Therefore, the detection and segmentation of organelles in scanning electron microscopy images is a common requirement in biological image analysis. However, due to differences in factors such as cell type, cell morphology, and cell density, the analysis of specific data often requires highly customized algorithms and inefficient and cumbersome manual operations. Therefore, this article proposes a new method for organelle segmentation in scanning electron microscopy images. This method first reduces the problem of noise in the image through Gaussian filtering technology, and then uses the Canny operator to perform edge detection on the image to preliminarily obtain the edges of organelles. Then, morphological operations such as dilation and corrosion, as well as connecting contours, are used to obtain more accurate organelle shapes and edge features. Finally, based on the outer rectangle features of the contours, the rectangles are deleted and merged, and the processed rectangles are used to label the recognized organelles. The method proposed in this article has certain innovation and practicality, which can effectively achieve automated segmentation and localization of organelles in scanning electron microscopy images, providing certain reference and reference value for research and application in related fields. © 2024 Copyright held by the owner/author(s).

关键词： image segmentation

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3rd International conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2023

3rd International Conference on Information Technologies and...

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3rd International conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2023

ISBN: (纸本)9783031603174

The proceedings contain 18 papers. The special focus in this conference is on Information Technologies and Intelligent Decision Making systems. The topics include: Comparative Analysis of Traditional Machine Learning Approaches for Time Series Clustering Under Colored Noise;on the Open Transport Data Analysis Platform;investigation of the Characteristics of a Frequency Diversity Array Antenna;comparative Analysis of Fuzzy Controllers in a Truck Cruise Control System;implementation of a Blockchain-Based Software Tool to Verify the Authenticity of Paper Documents;development of Methods and algorithms for Dimension Reduction of Space Description for Pattern Recognition Problem;service for Checking Students’ Written Work Using a Neural Network;implementing a Jenkins Plugin to Visualize Continuous Integration Pipelines;elimination of Optical Distortions Arising from In Vivo Investigation of the Mouse Brain;quantum Fourier Transform in image processing;choosing an Information Protection Mechanism Based on the Discrete Programming Method;application of Machine Learning Methods for Annotating Boundaries of Meshes of Perineuronal Nets;diagnostics of Animals Diseases Based on the Principles of Neutrosophic Sets and Sugeno Fuzzy Inference;the Technique of processing Non-Gaussian Data Based on Artificial Intelligence;development of Automation and Control System of Waste Gas Production Process Based on Information Technology;machine Learning and Data Mining.

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Parameters Optimization of Dynamic CT Pulmonary Perfusion Imaging Analysis Technology Based on Artificial Intelligence

Parameters Optimization of Dynamic CT Pulmonary Perfusion Im...

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2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Li, Chuangui Liu, Xiaocheng Zhang, Xinhui Zhou, Guijuan Zhu, Yuexiang First Affiliated Hospital of Hebei North University Department of Radiology Zhangjiakou China University of Chinese Medicine Dongzhimen Hospital of Beijing Department of Radiology Beijing China

ISBN: (纸本)9798350315455

In recent years, with the rapid development and popularization of artificial intelligence technology, CT has been more and more widely used in the field of clinical medicine as a non-volume micro body diagnostic method. In this paper, the artificial intelligence software is used to build a dynamic CTR lung scanning image based on the wavelet neural network model, and the genetic algorithm is used to improve the traditional visual lung image quality analysis model to improve the diagnostic efficiency. At the same time, the matrix representation weight value of the relationship between different types of disease maps is obtained by changing the threshold value to calculate the acoustic difference between the same CT tissue to determine its membership function and carry out joint optimization, After that, the effect of this technology was tested, and the results showed that the artificial intelligence based dynamic CT pulmonary perfusion imaging technology had faster generation time and higher quality than traditional algorithms in image processing, so as to achieve accurate mining of results and reduce the number of misdiagnosis. As a result, this method achieved 15 s for imaging test which comparatively lesser than Traditional imaging algorithms. © 2023 IEEE.

关键词： artificial intelligence dynamic CT lung image lung perfusion imaging parameter optimization

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