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

共 13662 条记录，以下是1031-1040 订阅

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Comparative analysis of three advanced deep learning algorithms for Multiple Sclerosis lesion segmentation in FLAIR MRI

Comparative analysis of three advanced deep learning algorit...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Yi Zhu Thomas Grenier Chantal Revol-Muller CREATIS University of Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

This paper addresses the pressing need for enhanced tools in the diagnosis and management of Multiple Sclerosis (MS), particularly in the accurate detection and segmentation of MS lesions. Leveraging recent advances in deep learning, we evaluate the performance of three state-of-the-art algorithms, focusing on their potential to improve both precision and efficiency in MS lesion segmentation from medical images. Our study provides critical insights into the strengths and limitations of each model, offering valuable guidance for future applications of AI in MS diagnosis and treatment.

关键词： Deep learning image segmentation Accuracy Multiple sclerosis Computational modeling Signal processing algorithms Signal processing Real-time systems Lesions Reliability

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Gaussian Blur through Parallel Computing

Gaussian Blur through Parallel Computing

引用

International conference on image processing and Vision Engineering (IMPROVE)

作者： Ibrahim, Nahla M. Abou ElFarag, Ahmed Kadry, Rania Arab Acad Sci & Technol & Maritime Transport Dept Comp Engn Alexandria Egypt

ISBN: (纸本)9789897585111

Two dimensional 2D convolution is one of the most complex calculations and memory intensive algorithms used in image processing. In our paper, we present the 2D convolution algorithm used in the Gaussian blur which is a filter widely used for noise reduction and has high computational requirements. Since, single threaded solutions cannot keep up with the performance and speed needed for image processing techniques. Therefore, parallelizing the image convolution on parallel systems enhances the performance and reduces the processing time. This paper aims to give an overview on the performance enhancement of the parallel systems on image convolution using Gaussian blur algorithm. We compare the speed up of the algorithm on two parallel systems: multi-core central processing unit CPU and graphics processing unit GPU using Google Colaboratory or "colab".

关键词： CUDA Parallel Computing image Convolution Gaussian Blur Google Colaboratory

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MR image Harmonization with Transformer 20

MR Image Harmonization with Transformer

引用

20th IEEE International conference on Mechatronics and Automation, ICMA 2023

作者： Han, Dong Yu, Rui Li, Shipeng Wang, Jing Yang, Yuzun Zhao, Zhixun Wei, Yiming Cong, Shan Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Harbin Engineering University Qingdao Innovation and Development Center Qingdao266520 China

ISBN: (纸本)9798350320831

Many clinical applications require medical image harmonization to combine and normalize images from different scanners or protocols. This paper introduces a Transformer-based MR image harmonization method. Our proposed method leverages the self-attention mechanism of the Transformer to learn the complex relationships between image patches and effectively transfer the imaging characteristics from a source image domain to a target image domain. We evaluate our approach to state-of-the-art methods using a publicly available dataset of brain MRI scans and show that it provides superior quantitative metrics and visual quality. Furthermore, we demonstrate that the proposed approach is highly resistant to fluctuations in image modality, resolution, and noise. Overall, the experiment results indicate that our approach is a promising method for medical image harmonization that can improve the accuracy and reliability of automated analysis and diagnosis in clinical settings. © 2023 IEEE.

关键词： Magnetic resonance imaging

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3D image Reconstruction and processing for Augmented and Virtual Reality Applications: A Computer Generated Environment 2

3D Image Reconstruction and Processing for Augmented and Vir...

引用

2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Baskar, Radhika Krishnammal, P. Muthu Aeron, Anurag Ali, Syed Sumera Leonid, T. Thomas Arun, M.R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India School of Computing Dit University Computer Science and Engineering Dehradun India Csmss Chh Shahu College of Engineering Electronics and Computer Engineering Department Maharashtra Aurangabad India Ece Dept Kcg College of Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350369960

New technologies like AR and VR are changing our online and offline experiences. Augmented and virtual reality applications need 3D picture reconstruction and processing. In augmented and virtual reality, this abstract explains 3D image reconstruction and processing concepts, techniques, and effects. Creating engaging AR and VR experiences starts with 3D data. Depth sensors, stereoscopic images, and photogrammetry are essential for accurate 3D reconstructions of real-world objects and settings. These data streams support spatial cognition and interaction in AR/VR. Reconstruction technologies like point cloud and mesh reconstruction are essential for turning 3D data into models. Virtual features that complement or replace the actual environment in AR experiences or build fully fabricated VR settings are based on these models. By applying 2D textures to 3D geometry, 'texture mapping' enhances scene realism. Realtime augmented and virtual reality processing requires effective rendering, tracking, and physics simulation algorithms. Rendering engines provide beautiful, responsive experiences, while SLAM lets users explore and interact with virtual information in real life. Occlusion culling and level-of-detail rendering are two optimisation methods needed for smooth performance on high-end VR systems and mobile AR devices. Virtual reality is defined by 3D object interaction and control. Natural and engaging interfaces are created using gesture detection and real-time physics simulations. AR and VR software developers encounter new challenges with each iteration. AR requires accurate spatial mapping, dependable tracking, and simple user interfaces to blend the virtual and real worlds. In contrast, virtual reality (VR) immerses users in realistic computer settings. Portability across operating systems is crucial for greater adoption. Developers must optimise programmes for several hardware platforms to reach the most people. © 2023 IEEE.

关键词： 3D image Augmented reality Reconstruction Virtual Reality

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Comparison of feature selection algorithms for Data classification problems 8

Comparison of feature selection algorithms for Data classifi...

引用

8th International conference on Information Technology and Nanotechnology, ITNT 2022

作者： Tislenko, M.D. Gaidel, A.V. Kupriyanov, A.V. Samara National Research University Samara Russia Image Processing Systems Institute RAS-Branch of the FSRC 'Crystallography and Photonics' RAS Samara Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

This article discusses various feature selection algorithms, namely SelectKBest with different statistical criteria and Random Forest algorithm, compares classification accuracy with and without feature selection algorithms on different datasets. As a result, it was found that in the general case, Random Forest algorithm shows the best classification, but on some datasets it is better to use SelectKBest with certain statistical criteria. © 2022 IEEE.

关键词： Classification (of information)

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Robust Convolutional Neural Network for image Classification with Gaussian Noise 9

Robust Convolutional Neural Network for Image Classification...

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9th International conference on Fuzzy systems and Data Mining, FSDM 2023

作者： Surono, Sugiyarto Arofah, Dyiyah Kresna Eni Thobirin, Aris Department of Mathematic Fast Universitas Ahmad Dahlan Yogyakarta Indonesia

ISBN: (纸本)9781643684703

A Convolutional Neural Network (CNN) is one branch of Deep Learning widely used for image classification. CNN have complex architectures and capable of achieving high accuracy and producing good results. However, CNN have limitations, especially when dealing with noisy images. image noise can decrease classification performance and increase network training time. This research was tested the robustness of two CNN methods, namely VGG16 and ResNet50, in processing images with added Gaussian noise at various levels, without any preprocessing. The dataset used is the Rice image Dataset, which consists of images of 5 different types of rice. The data is divided into three parts: 70% for training, 20% for testing, and 10% for validation. The results of this study show that as the variability in generating Gaussian noise in the images increases, the loss function value consistently increases, and the accuracy decreases. However, the increase in the loss function value and the decrease in accuracy are not significantly different among the different levels of noise variability. © 2023 The authors and IOS Press.

关键词： image classification

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RGB image Guided Depth Hole-Filling Using Bidirectional Attention Mechanism 1

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17th International conference on Intelligent Information Hiding and Multimedia Signal processing (IIH-MSP) / 14th International conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Chen, Anran Zhao, Yao Lin, Chunyu Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (数字)9789811910531

ISBN: (纸本)9789811910531;9789811910524

Depth maps obtained by commercial depth sensors are more likely to have missing values due to the occlusion effect, low-reflection objects, etc. Filling holes of depth maps is an important way to meet the demands of depth related computer vision tasks. In this paper, we propose an efficient end-to-end network that takes RGB image and mask of the hole to jointly guide the depth hole-filling. Previous algorithms indistinguishably treat valid pixels and holes, resulting in inaccurate depth values prediction and blurred boundaries. Nevertheless, the proposed algorithm uses the bidirectional attention mechanism which takes the surrounding valid values as the auxiliary information to focus on the process of depth hole-filling from edge to center. The proposed method achieves competitive performance on existing public datasets.

关键词： Bidirectional attention mechanism Depth map Hole-filling RGB guided

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Recent advancements in image Steganography using Generative Adversarial Networks methods

Recent advancements in Image Steganography using Generative ...

引用

Intelligent algorithms for Computational Intelligence systems (IACIS), International conference on

作者： Syeda Imrana Fatima Yugandhar Garapati Department of Computer Science & Engineering GITAM (Deemed to be University) Hyderabad India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Generative Adversarial Networks are employed by GAN-based models in image steganography to efficiently conceal information from images while preserving their visual integrity. These models are made up of two primary parts: the discriminator which determines if an image is a real image or a stego-image, and the generator which embeds the hidden data into images. The generator learns how to embed data in a way that minimizes obvious distortions through adversarial training, where the discriminator and generator fight to make the hidden data less noticeable to detection algorithms and human observers. High data capacity and versatility are strengths of GAN-based steganography models, which enable efficient data concealment across a variety of image formats and steganographic techniques. They dynamically adjust the embedding procedure to strike a compromise between image quality and data capacity. They do, however, come with certain drawbacks such as the need for sophisticated model training, high processing overhead and hyperparameter sensitivity. Notwithstanding these drawbacks, GAN-based techniques mark a substantial breakthrough in the development of delicate and reliable image steganography techniques.

关键词： Training image quality Steganography Visualization Sensitivity Observers Generative adversarial networks Generators Data models Reliability

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Bidirectional Iterative Diffusion Encryption Algorithm Based on Improved Chaotic systems

Bidirectional Iterative Diffusion Encryption Algorithm Based...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Juan Wang Boyong Gao College of Information Engineering China Jiliang University Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Chaotic systems are widely used in cryptography due to their traversability, unpredictability and sensitivity to initial values. However, the traditional low-dimensional chaotic system has the disadvantages of small key space, short iteration period and easy to be constructed, which affects the security of encryption. Therefore, in order to solve this problem, this paper constructs a new one-dimensional chaotic mapping, which is experimentally shown to have wider chaotic range, better unpredictability and larger key space. On this basis, this paper proposes an encryption scheme that can encrypt different images of different sizes by iteratively diffusing the pixels in both directions through chaotic sequences, and the experimental results show the security and effectiveness of the algorithm.

关键词： Computer vision Sensitivity Correlation Chaotic mapping image processing Machine learning Color Iterative algorithms Encryption

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processing and Classification algorithms of Medical images

Processing and Classification Algorithms of Medical Images

引用

International image processing, Applications and systems conference (IPAS)

作者： Oltiana Seitaj Department of Industrial Engineering Electronics Mechanics University of Roma Tre Rome Italy

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional image processing techniques with advanced AI-driven models. The study integrates Convolutional Neural Networks (CNNs) with preprocessing methods such as noise reduction, adaptive thresholding, and contrast enhancement to address challenges associated with complex anatomical structures and variability in medical image quality. A novel hybrid framework is proposed, leveraging traditional methods to preprocess data and improve input quality for deep learning models, ultimately enhancing segmentation accuracy and *** effectiveness of the proposed approach is assessed using quantitative performance metrics, including Dice Similarity Coefficient (DSC), Hausdorff Distance, Jaccard Index, Precision, and Recall. Preliminary results indicate significant improvements in tumor boundary detection and reduced false-positive rates compared to existing methods. By streamlining segmentation workflows and enabling near-realtime applications in clinical settings, this research offers a pathway to improved diagnostic accuracy, treatment planning, and workflow *** implications include the potential for integration into clinical imaging pipelines, fostering advancements in computer-assisted diagnosis and personalized treatment strategies. This study underscores the value of hybrid methodologies in addressing current limitations and paving the way for more precise and efficient medical image segmentation.

关键词： Deep learning image segmentation Adaptation models Accuracy Thresholding (Imaging) Streaming media Reliability MATLAB Biomedical imaging Tumors

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