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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121-130 订阅

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Fundus image Classification for Glaucoma using U-Net Architecture and Logistic Regression 2

Fundus Image Classification for Glaucoma using U-Net Archite...

引用

2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Bajaj, Vanshika Kotambkar Shelke, Deepali M. Shri Ramdeobaba College of Engineering and Management Dept. of Information Technology Nagpur India

ISBN: (纸本)9798350310719

The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas. © 2023 IEEE.

关键词： Computer aided diagnosis

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Breaking Through Color Casts: Enhancing image Fidelity with Machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

引用

2024 International conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Reddy, Choppa Jeevan Sai Akshay, Bavani Bhawane, Bange Reddy, Busireddy Parvathammagari Srikanth Satish, Addanki Sree Vidyanikethan Engineering College Tirupati India School of Engineering Mohan Babu University Tirupati Dept. of Ece India

ISBN: (纸本)9798350375237

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques. © 2024 IEEE.

关键词： image enhancement

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

引用

4th International conference on Intelligent systems and Pattern Recognition-ISPR

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

ISBN: (纸本)9783031821523;9783031821530

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.

关键词： Deep Learning image processing image Denoising Convolutinal Neural Networks (CNNs) Blind Noise DnCNN DRNet RIDNET PSNR SSIM

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Robust image watermarking scheme based on orientation of local image texture 16

Robust image watermarking scheme based on orientation of loc...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Li, Qiutong Zhang, Qiyuan Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

With the rapid development of big data and Internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust watermarking technology can effectively resist various attacks and protect image copyright. Most of the existing robust watermarking schemes generally adopt the global embedding method and often ignore the texture orientation of the cover image itself leading to poor imperceptibility and robustness. In this paper, we propose an innovative digital image robust watermarking scheme. Firstly, we conduct the Oriented FAST and Rotated BRIEF (ORB) and Speeded Up Robust Features (SURF) feature point detection algorithms, combined with the texture complexity of the cover image to select the most complex texture region as the local embedding location of the watermark. Secondly, to further improve the robustness of the watermarked image, we conduct the Discrete Cosine Transform (DCT) adaptive inter-block correlation algorithm. We embed the watermark into the cover image by adjusting the difference between the image embedding block and the adjacent block. We have tested a dataset containing multiple images, in which various common attacks are simulated and multiple cover images are tested. By comparing the experimental results, our scheme performs better regarding robustness and imperceptibility. © 2025 SPIE.

关键词： Cosine transforms

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processing and Classification algorithms of Medical images 6

Processing and Classification Algorithms of Medical Images

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

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

ISBN: (纸本)9798331506520

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. © 2025 IEEE.

关键词： Convolutional neural networks

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Refined Coati Optimizer with Chaotic systems for Optimization in image processing Applications 3

Refined Coati Optimizer with Chaotic Systems for Optimizatio...

引用

3rd International conference on Embedded systems and Artificial Intelligence, ESAI 2024

作者： Mansouri, Hanaa El-Khanchouli, Karim Tahiri, Mohamed Amine Ghouate, Nawal El Sayyouri, Mhamed Moustabchir, Hassane Sidi Mohamed Ben Abdellah University Engineering Systems and Applications Laboratory National School of Applied Sciences Fez Morocco

ISBN: (纸本)9798331522056

The Coati Optimization Algorithm (COA) is a promising metaheuristic inspired by coati hunting behaviors, demonstrating strong performance in complex optimization tasks. However, COA encounters challenges with convergence speed and is susceptible to getting trapped in local optima. This paper introduces the Refined Coati Optimizer (R-CO), an enhanced COA variant incorporating chaotic systems to improve exploration and exploitation phases. By embedding chaotic maps, R-CO increases search diversity, accelerates convergence, and reduces the likelihood of premature convergence. Extensive testing on 23 benchmark functions-covering unimodal, multimodal, and fixed-dimension multimodal problems-confirms the algorithm's robust performance. R}-CO is also applied to an image segmentation task, where it outperforms the original COA and other leading metaheuristic algorithms. Results highlight R-CO's enhanced optimization capabilities, positioning it as a valuable tool for theoretical and applied optimization challenges. © 2024 IEEE.

关键词： Heuristic algorithms

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Face Mask Detection in Real Time using Machine Learning algorithms 3

Face Mask Detection in Real Time using Machine Learning Algo...

引用

3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Kavitha, R. Sultania, Tanishq Padhmashri, V. Shreya, A. Kumaraguru College of Technology Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798331509675

Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made it easier to identify if an individual is wearing a face mask or not. HAAR-CASCADE and ResNet10 algorithms are applied for initial face recognition and then Mobilenet V2 and VGG16 algorithm are employed for recognizing face masks in gatherings. Amongst the stages involved in accomplishing the AI training are the pre-processing augmenting data, training, testing, and segmenting images. During each step, the CNN algorithms are utilized to generate an image segmentation of the input dataset highlighting those wearing masks and those without mask. The model is then trained with dataset, Mobilenet V2 yields an accuracy of 96.6% and VGG16 yield an accuracy of 99.42% in detecting individuals with and without wearing masks. VGG16's deep architecture, feature extraction and transfer learning abilities enable it to be highly precise and effective. However, VGG16 is highly resource intensive and needs extensive computation time. © 2025 IEEE.

关键词： image segmentation

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Real-Time Scratched image Restoration Using Neural Networks 2

Real-Time Scratched Image Restoration Using Neural Networks

引用

2nd International conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Sharma, Harsh Pradhan, Rajeshwari Mogha, Harsh Singh, Shivpratap Kumar, Rupesh Computer Science and Engineering Chandigarh University Punjab India

ISBN: (纸本)9798331519056

Real-time image restoration is a cutting-edge tech-nology that accelerates or rebuilds images as soon as they are captured, processed, or released to correct issues such as noise, blur, and compression. This field is of great importance in applications such as medical imaging, surveillance, autonomous vehicles. and live video streaming where sharpness and image quality are fundamental The main challenge lies in developing algorithms that can quickly and efficiently restore image quality. It balances the need for high-speed processing with the need for precision and adaptability to different conditions. Advanced techniques Including deep learning and convolutional neural networks (CNN) are often used to achieve these goals. Moreover, it is essential to optimize these algorithms for real-time pro-cessing. Real-time performance in constrained hardware such as mobile devices or embedded systems As real-time image recovery technology continues to develop, It thus promises to significantly improve the quality of image data in a variety of domains. They provide sharper, more reliable images for critical applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Application of image Recognition algorithms Based on Deep Learning in the Field of Communication Science

Application of Image Recognition Algorithms Based on Deep Le...

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

作者： Su, Yunxia Shanghai Publishing and Printing College Shanghai China

ISBN: (纸本)9798350315455

With the popularization of the internet, people have paid more and more attention to image processing, and deep learning has become a research hotspot. Analyzing images based on deep neural networks is a very valuable and promising direction for development. This article first introduced text representation methods and two commonly used classifiers in machine learning, namely the manual annotation model method. Then, it elaborated on the effectiveness and current application status of a fusion of multi-layer feedforward technology and convolutional neural networks for edge detection, feature extraction, and other fields. Finally, a new concept was proposed, and deep learning was applied to explain the important significance of image preprocessing in digital music file classification. Afterwards, a communication field training model based on image recognition algorithms was simulated and tested for its functionality. The test results showed that the image recognition algorithm based on deep learning had good noise processing performance during the propagation process, with an image clarity rate of over 90%. Through deep learning algorithms, automatic recognition and classification of radio signals can be achieved, including determining signal type, modulation method, frequency range, and other information. This is of great significance for radio spectrum monitoring, spectrum management, and interference source localization. By using deep learning algorithms, classification models for different types of radio signals can be trained, making the recognition of radio spectrum more accurate and reliable. © 2023 IEEE.

关键词： communication field deep learning image recognition algorithms neural networks

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ICMSSP 2024 - 2024 9th International conference on Multimedia systems and Signal processing

ICMSSP 2024 - 2024 9th International Conference on Multimedi...

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9th International conference on Multimedia systems and Signal processing, ICMSSP 2024

ISBN: (纸本)9798400716911

The proceedings contain 8 papers. The topics discussed include: multi-scale hierarchical VQ-VAEs for blind image inpainting;intelligent monitoring of surface roughness and straightness with roundness on CNC turning utilizing wavelet transform via neural networks;research on ECG signal denoising based on EM-UKF algorithm;an efficient framework to recognize deepfake faces using a light-weight CNN;implementation of ray tracing algorithms and their application to graphics rendering;a two-stage method with diffusion models for single-image view synthesis;enhanced potato detection using an improved YOLOv8 algorithm;and real-time cat breed identifier using OpenCV and YOLOv5.

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