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检索条件"任意字段=Conference on Applications of Digital Image Processing XXII"

共 10272 条记录，以下是11-20 订阅

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Automated retinal disorders classification: leveraging digital image enhancement techniques and deep learning on OCT images 47

Automated retinal disorders classification: leveraging digit...

引用

conference on applications of digital image processing XLVII

作者： Nigjeh, Mahdi Kargar Nigjeh, Mahsa Kargar Umbaugh, Scott E. Southern Illinois Univ Edwardsville Comp Vis Proc Lab Elect & Comp Engn Edwardsville IL 62026 USA

ISBN: (纸本)9781510679344;9781510679351

Retinal Optical Coherence Tomography (OCT) plays a pivotal role in diagnosing ocular disorders by providing detailed imaging of retinal layers. However, the analysis process remains time-consuming, posing a challenge to its widespread use. This study investigates the integration of Artificial Intelligence (AI) to streamline the analysis of OCT images. Employing Deep Learning (DL) models-VGG16, ResNet18, DenseNet-transfer learning, and data augmentation, the research aims to enhance OCT images, optimize disease recognition, and accurately classify CNV (Choroidal Neovascularization), DME (Diabetic Macular Edema), DRUSEN, and NORMAL pathologies. The dataset undergoes preprocessing, resizing, and enhancement to refine the images. The DenseNet model achieved the highest test accuracy of 92.41% after 25 epochs, demonstrating its potential in efficiently diagnosing ocular pathologies through OCT images.

关键词： digital image processing Optical Coherence Tomography X-Ray Deep Learning Medical image Analysis image Enhancement Retinal Disorders

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A Comparative Analysis of digital image Forgery Detection Methods 5th

A Comparative Analysis of Digital Image Forgery Detection Me...

引用

5th International conference on Data Science, Machine Learning and applications

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Reddy, H. Venkateshwara Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad India Vardhaman Coll Engn Dept Informat Technol Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

digital image Forgery Detection refers to the process of identifying whether a digital image has been manipulated or altered in any way, with the intention of deceiving or misleading the viewer. digital image forgery detection has become an increasingly important research area due to the widespread availability of powerful image editing tools and the potential misuse of manipulated images. In this survey paper, we present a comprehensive comparative analysis of various digital image forgery detection methods. Our objective is to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the existing approaches and their effectiveness in detecting different types of image forgeries. The survey begins by outlining the different types of digital image forgeries, including copy-move, splicing, retouching, and more. We then proceed to review and compare a range of forgery detectionmethods proposed in the literature. These methods include traditional techniques based on handcrafted features, as well as more recent approaches that leverage advanced machine learning and deep learning algorithms. For each method, we discuss its underlying principles, key components, and specific algorithms utilized. We also analyse the strengths and limitations of each method, considering factors such as detection accuracy, robustness to image transformations, computational efficiency, and the ability to handle complex forgery scenarios. Additionally, we examine the evaluation metrics employed to assess the performance of these methods, including detection accuracy, localization accuracy, processing time, and other relevant metrics. Through our comparative analysis, we identify common trends, challenges, and advancements in the field of digital image forgery detection. We highlight the trade-offs between different methods and provide insights into their suitability for specific forgery types and real-world applications. Furthermore, we discuss the limitations

关键词： image digital Forgery Detection

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Interpreting microscopic structures in virtually stained histological sections for veterinary oncology applications 22

Interpreting microscopic structures in virtually stained his...

引用

conference on Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems xxii

作者： Duplevska, Diana Maliks, Romans Tamosiunas, Mindaugas Melderis, Mikus Viskere, Daira Cugmas, Blaz Kadikis, Roberts Matise-van Houtana, Ilze Inst Elect & Comp Sci Dzerbenes 14 LV-1006 Riga Latvia Univ Latvia Inst Atom Phys & Spect 19 Raina Blvd LV-1586 Riga Latvia Latvia Univ Life Sci & Technol Preclin Inst Fac Vet Med K Helmana 8 LV-3004 Jelgava Latvia

ISBN: (纸本)9781510669222;9781510669215

We employed a Pix2Pix generative adversarial network to translate the multispectral fluorescence images into colored brightfield representations resembling H&E staining. The model underwent training using 512x512 pixel paired image patches, with a manually stained image serving as the reference and the fluorescence images serving as the processing input. The baseline model, without any modifications, did not achieve high microscopic accuracy, manifesting incorrect color attribution to various biological structures and the addition or removal of image features. However, through the substitution of simple convolutions with Dense convolution units in the U-Net Generator, we observed an increase in the similarity of microscopic structures and the color balance between the paired images. The resulting improvements underscore the potential utility of virtual staining in histopathological analysis for veterinary oncology applications.

关键词： virtual staining generative adversarial networks H&E staining fluorescence histological evaluation veterinary oncology

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image Denoising: A Comparative Study of Convolutional Neural Networks 4th

Image Denoising: A Comparative Study of Convolutional Neural...

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4th International conference on digital Technologies and applications (ICDTA)

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci Lab M2PA Fes 30030 Morocco

ISBN: (纸本)9783031686528;9783031686535

image processing is a vigorous area of study that utilizes various algorithms to manipulate, analyze, and enhance digital images. image denoising is one of the crucial applications of image processing. Still, the occurrence of image noise is inevitable due to various sources, including low light conditions, high ISO settings, and transmission artifacts, necessitating the availability of denoising techniques to significantly improve visual image quality. This is particularly important in fields such as computer vision, medical imaging and remote sensing. Not only does it facilitate image analysis by retaining important details, but it also optimizes the performance of compression algorithms, improves storyteller detection. In this project, we propose an in-depth study of image denoising, focusing on the use of convolutional neural networks (CNNs). The problem of Gaussian noise will be treated by applying different levels of s (low sigma = 15, medium sigma = 25, and high sigma = 50). During this project, a full comparative analysis will be made with the three mainCNNarchitectures: DnCNN, RIDNet, and IRCNN, illustrative of the quantitative and qualitative experimental results obtained by these different approaches. In fact, these approaches have shown impressive performance in image processing tasks, including image denoising, since they used different techniques that can be adopted in CNN, such as regularization methods, batch normalization, and residual learning.

关键词： image Denoising Convolutional Neural Networks (CNNs) DnCNN RIDNET IRCNN Noise level PSNR SSIM

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On Handcrafted Machine Learning Features for Art Authentication 8

On Handcrafted Machine Learning Features for Art Authenticat...

引用

IEEE 8th International conference on Signal and image processing applications (IEEE ICSIPA)

作者： Bwanali, M. Ugail, H. Mnasri, Z. Jensen, E. Omar, Z. Univ Bradford Ctr Visual Comp & Intelligent Syst Bradford W Yorkshire England Univ Teknol Malaysia Fac Elect Engn Johor Baharu Malaysia

ISBN: (纸本)9798350352368

Accurate art authentication is crucial for ensuring correct attribution, cultural preservation, and financial valuation of artworks. Machine learning tools can significantly complement established techniques such as connoisseurship, provenance research, and scientific analysis (e.g., pigment analysis and radiographic imaging). This study explores a more objective and explainable approach to machine learning in art authentication. Specifically, we investigate the use of handcrafted features, such as image entropy, energy, texture patterns, and image similarities, to train a OneClassSVM model based on known authentic images of paintings by a given artist. To demonstrate the versatility of our method, we present results for three selected artists: John Constable, Vincent van Gogh, and Jean-Michel Basquiat. We further show by way of an example how our method can be utilised to study a given painting in terms of its attribution to a given artist. These examples, along with the accuracy of our results, indicate the potential of the proposed method as a flexible and efficient tool for art authentication alongside more established methods.

关键词： Art Authentication One Class SVM Machine Learning digital Heritage

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JPEG Trust: an international standard facilitating the assessment of trustworthiness of digital media assets 47

JPEG Trust: an international standard facilitating the asses...

引用

conference on applications of digital image processing XLVII

作者： Temmermans, Frederik Caldwell, Sabrina Bhowmik, Deepayan Ebrahimi, Touradj Vrije Univ Brussel Brussels Belgium IMEC Brussels Belgium Australian Natl Univ Canberra ACT Australia Univ Stirling Stirling Scotland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

JPEG Trust is a new international standard that responds to the pressing need of assessing trust in digital media assets. JPEG Trust provides a comprehensive framework addressing key elements such as provenance, authenticity, integrity, and copyright declaration. Built on top of established JPEG and other industry standards, the framework ensures compatibility across digital media ecosystems. This paper provides an overview of the JPEG Trust framework and demonstrates its potential to enhance the reliability and trustworthiness of digital media content.

关键词： JPEG Trust media authenticity provenance trust interoperability JPEG standardization

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FPGA Implementation of Elliptic Curve digital Signature Algorithm Based on PUFs

FPGA Implementation of Elliptic Curve Digital Signature Algo...

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9th International conference on Signal and image processing (ICSIP)

作者： Zhang, Hao Liu, Xuankai Li, Bing Southeast Univ Sch Integrated Circuit Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350350920

In recent years, with the explosive growth of information, incidents threatening information security such as significant information leaks and personal privacy attacks have been constantly emerging. Research on public key cryptography has become an effective means to ensure security. Elliptic curve cryptography, due to its ability to provide higher security and lower computational complexity, has gradually become the mainstream asymmetric encryption method in public key encryption systems. Its corresponding Elliptic Curve digital Signature Algorithm (ECDSA) has also gradually become a mainstream digital signature scheme. However, ECDSA often faces issues such as private key leakage and reuse of random numbers, leading to security risks. Physical Unclonable Functions (PUFs) with real-time reading, unpredictability, and non-clonability characteristics can effectively address the security issues caused by private key storage. This paper proposes a True Random Number Generator (TRNG) based on PUFs to address the security issues of ECDSA. The TRNG has passed NIST tests. The system adopts the spec256k1 curve, and experiments show that the entire system consumes 6.33ms for a single signature operation and 6.43ms for a single verification operation at a clock frequency of 100MHz. The entire system is implemented on Xilinx Zynq-7000 FPGA.

关键词： Elliptic Curve digital Signature Algorithm Physical Unclonable Functions True Random Number Generator FPGA

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Bibliometric Review: characterizing wine grapes through image processing 47

Bibliometric Review: characterizing wine grapes through imag...

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conference on applications of digital image processing XLVII

作者： Ana Karena, Peraza Munoz Carlos Adolfo, Vasquez Rochin Miguel Enrique, Martinez Rosas De Avila Humberto, Cervantes Univ Autonoma Baja California Fac Ingn Arquitectura & Diseno Elect Carretera Transpeninsular Ensenada Tijuana 3917 Ensenada 22860 Baja California Mexico

ISBN: (纸本)9781510679344;9781510679351

This work aims to show the latest scientific advances in digital image processing applied to wine grape characterization. The literature review highlights in existing studies about external and internal grape characteristics, but often rely on expensive equipment and lack of essential features for robust predictive results. Consequently, experts in the field advocate for new studies to widen the range of characteristics and economic viability for end-users. A systematic literature analysis of the Scopus database, using keywords like "grape image processing", identified 290 papers from 2012 to 2023. Additionally, advanced searches related to maturation, color, chemical analysis, phenolic composition, sugar content, prediction models, and correlation of physical and chemical attributes indicate an area of opportunity due to the decrease of works found on these specific topics. Nevertheless, bibliometric results reveal the evolving research landscape in these areas over the past decade, with notable authors such as Whitty, M., and Liu, S. Leading institutions and countries include China, India, the United States, and Spain. The VOSviewer software was used to confirm influential studies, prominent research groups, and emerging trends in the field. Finally, as a case study, the initial results of our work are presented, which aims to characterize the physical-chemical parameters of wine grapes in different stages of ripening, through digital processing of grape images.

关键词： Grapes images processing wine production grape quality grape ripening grape yield bibliometric review

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Modeling Camera ISP Pipeline with Deep Learning 31

Modeling Camera ISP Pipeline with Deep Learning

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31st IEEE conference on Signal processing and Communications applications (SIU)

作者： Erbas, Koray Ugur Celebi, Aysun Tasyapi Kocaeli Univ Elekt & Haberlesme Muhendisligi Kocaeli Turkiye

ISBN: (纸本)9798350343557

Today, images presented to the users by digital cameras are created by sequentially processing the raw data received from the sensor by different signal processing modules. In this traditional approach, in which the signal processing modules are designed independently of each other in accordance with the purpose, the final image quality might be adversely affected due to distortion effects occurred at modules and carried throughout the camera pipeline. In order to eliminate these effects and to ensure the optimization of the camera bus from beginning to end, it is seen that researches on modeling the camera pipeline with deep network architectures have increased in recent years. Within the scope of this paper, the images in the Zurich dataset created for this purpose were edited using Topaz Gigapixel AI and Photomatix Pro 5.0 software, and the final model performance was examined by retraining the CNN (Convolutional Neural Network) based PYNET[1] network architecture, which is one of the pioneering studies in this field. Considering the PIQUE (Perception based image Quality Evaluator) metric, which does not need a reference image, the performance of the created model is increased;at the same time, it has been seen that HDR tonemapped image can be constructed by revealing the details in the low and high exposure regions.

关键词： image processing Kamera ISP Veri Yolu Deep Learning CNN

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Application of CNN in Visual image Design: image Feature Extraction and processing 24

Application of CNN in Visual Image Design: Image Feature Ext...

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International conference on Machine Intelligence and digital applications (MIDA)

作者： Jin, Huijiao Wang, Fei Jiang, Lanying Tian, Meng Hainan Technol & Business Coll Sch Architectural Engn Haikou 570203 Hainan Peoples R China Ningbo Univ Univ Angers Joint Inst Dept Fash Design Ningbo 315211 Zhejiang Peoples R China Harbin Guangsha Coll Arts & Media Sch Harbin 150000 Heilongjiang Peoples R China

ISBN: (纸本)9798400718144

In response to the problems of insufficient personalization and unclear feature extraction in traditional visual image design, this paper studied an image feature extraction and processing method based on CNN (Convolutional Neural Networks). Firstly, the images were preprocessed and enhanced to enhance the generalization ability and feature extraction performance of the CNN model;a lightweight CNN architecture and MobileNet (Mobile Network) were designed to reduce computational complexity and parameters, and the model was optimized through technical means;generative adversarial networks (GANs) were utilized to integrate creative elements and enhance the innovation of advertising design. Through transfer learning, the CNN model was adjusted, and the Adam optimizer and early stop strategy were adopted for model training and optimization. In the experiment of feature extraction accuracy, the accuracy of the model in this paper reached 98.8%, 97.8%, and 98%, and it was also better than the comparison model in the comparative experiment. In the generalization ability test, the model did not overfit and demonstrated good generalization ability;in the processing time test, the architecture used in this paper had a shorter processing time than other architectures;in the survey on the integration effect of creative elements, the attractiveness rating reached 96% of the positive reviews.

关键词： Visual image Design Convolutional Neural Networks image Feature Extraction and processing Mobile Network Generative Adversarial Networks

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