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

共 12594 条记录，以下是31-40 订阅

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A Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

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2025 International conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Syahda, Tabina Shafa Nabila Ma'ady, Mochamad Nizar Palefi Lidiawaty, Berlian Rahmy Telkom University Surabaya Department of Information Systems Surabaya Indonesia

ISBN: (纸本)9798331513320

Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377;0.9150;0.9143;0.9129;and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems. © 2025 IEEE.

关键词： Contrastive Learning

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Research on Radar Imaging Method Based on Tensor Neural Network 16

Research on Radar Imaging Method Based on Tensor Neural Netw...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Qiu, Tian Liu, Nan Yang, Minglei National Key Laboratory of Radar Signal Processing Xidian University Xian710071 China

ISBN: (纸本)9781510689251

LFM signals, as the most commonly used form of signals in radar systems, are of significant research value. At this stage, compressed sensing (CS) has significant potential to improve the image quality of radar imaging. However, the existing CS theories have limited ability to improve the imaging capability of LFM signals, and always suffer from low imaging efficiency, parameters need to be manually adjusted, and poor suppression of side flaps. In this paper, we propose a neural network imaging algorithm based on tensor compressed perception, called 2D-ISTA-Net. this algorithm accomplishes distance-azimuth 2D radar imaging by combining a model-based iterative threshold shrinkage (ISTA) algorithm and a data-driven deep learning model. First, the sparse recovery model of the LFM signal is obtained analytically, and then, the 2D-ISTA optimization algorithm is unfolded into a deep network structure in which all parameters are optimized based on data-driven auto-learning instead of manual tuning. Subsequently, the network learning is trained on the scattering point images in the training set to obtain a 2D-ISTA-Net network with autonomous parameter tuning capability. Furthermore, we compared this algorithm with some existing CS algorithms. The data show that 2D-ISTA-Net significantly improves the computing power and has a lower side flap level compared with the traditional CS algorithm. Experiments show that 2D-ISTA-Net has good reconstruction performance. © 2025 SPIE.

关键词： Radar imaging

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Synergistic Integration of MRI Prior for Enhanced Photoacoustic Tomography: Advancing Biomedical Imaging Quality 12th

Synergistic Integration of MRI Prior for Enhanced Photoacous...

引用

12th International conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Krishna Satya Varma, M. Yamini Priyanka, S. Department of IT SRKR Engineering College Chinaamiram Andhra Pradesh Bhimavaram India

ISBN: (纸本)9789819601424

In this research endeavor, we introduce a pioneering strategy to enhance the fidelity of photoacoustic tomography (PAT) images, addressing prevalent artifacts and distortions stemming from acoustic and optical properties of biological tissues. Our approach capitalizes on the synergy between PAT and magnetic resonance imaging (MRI), exploiting MRI's wealth of information on tissue anatomy and composition to refine PAT reconstructions. Through a meticulously designed framework, we integrate advanced image processing methodologies, spanning registration, fusion, and deep learning-based algorithms, to seamlessly merge MRI priors into the PAT reconstruction pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image fusion

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Post-processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： image segmentation

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Advanced HLS for Reconfigurable image processing in Big Data systems 12th

Advanced HLS for Reconfigurable Image Processing in Big Da...

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12th International conference on Big Data and Artificial Intelligence, BDA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Noida201309 India

ISBN: (纸本)9783031818202

This paper introduces novel HLS techniques for reconfigurable and memory-efficient image processing within deep learning frameworks, addressing inherent limitations of current deep learning accelerators (DLAs) due to their dependency on the host processor, leading to pipeline delays and reduced throughput. The proposed approach emphasizes parallelization, on-chip buffering, and scalability, enhancing computational efficiency and throughput for AI applications in big data and edge computing environments. Methodologically, the paper emphasizes mathematical analysis, hardware architecture specification, HLS modeling, Verilog RTL implementation. By addressing these challenges, this work contributes to advancing the performance capabilities of DLAs in deep learning inference tasks. The algorithms integrates seamlessly with DLA IP subsystems, such as AXI interfaces, external (DRAM) and internal (SRAM) memory interfaces, and activation/pooling engines. Key contributions include advanced algorithm design to enhance memory efficiency and reconfigurability, essential for adapting to diverse hardware architectures. Experimental validation demonstrates substantial improvements in computational speed and resource utilization compared to traditional methods, making it suitable for intensive AI tasks in big data analytics. In conclusion, this research presents a framework for integrating HLS techniques into hardware accelerators, facilitating efficient image processing across diverse applications in big data and edge computing. These advancements aim to bolster DLAs’ capabilities in handling complex AI workloads effectively, promising enhanced solutions for AI-driven domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Fractal image Compression Using Discrete Cosine Transform with Domain Classification and Hexagonal Partitioning

Fractal Image Compression Using Discrete Cosine Transform wi...

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International conference on Power Engineering and Intelligent systems, PEIS 2024

作者： Kulkarni, Milind Bharadwaj, Rakhi Joshi Hedaoo, Anushka Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819767090

image compression has seen much advancement over the years, with new algorithms and techniques being developed to make image files smaller and easier to transmit and store using both lossy compression methods as well as lossless compression techniques. This paper proposes a lossy fractal image compression method using hexagonal partitioning and domain classification on the quantized image pixels to alleviate the image compression technique's processing complexity. Fractal image compression is a technique for compressing digital images that uses the idea of self-similarity, which is the concept that a pattern can repeat itself at different scales. Our proposed method consists of transforming the spatial domain to frequency domain using discrete cosine transform with quantization and then applying hexagonal partitioning on the square image pixels. A range block is compared with every other classified domain block and the domain block having the least root mean squared (RMS) error is mapped to the range block and replaced with it, storing the original contrast and brightness settings. In this manner, an encoding file is created which stores information of all the mappings, and then the original image is decoded iteratively until the output resembles the original image. To evaluate the performance of the suggested algorithm, numerous simulations are performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete cosine transforms

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Quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

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2024 International conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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An Alternative Nonlinear Diffusion Algorithm for image Denoising and Deblurring 4th

An Alternative Nonlinear Diffusion Algorithm for Image Deno...

引用

4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Fatone, Lorella Funaro, Daniele Università di Camerino Via Madonna delle Carceri 9 Camerino62032 Italy Università di Modena e Reggio Emilia Via Campi 103 Modena41125 Italy

ISBN: (纸本)9783031812439

We discuss some applications in image restoration and enhancement, such as denoising and deblurring. The treatment involves a processing algorithm where an image is represented in a continuous frame and is manipulated through suitable time-dependent nonlinear Partial Differential Equations (PDEs). For this purpose, an ad-hoc space discretization scheme is implemented. As in the classic PDE-based image processing methods, the idea is to smooth out the regions of almost uniform shade and emphasize the discontinuities. The proposed methodology is quite efficient as satisfactory results are obtained after only one step. We present some numerical results by examining examples corrupted by average blur and/or salt and pepper noise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Salt and pepper noise

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Bone Tumor Detection and Classification Using GoogleNet Algorithm

Bone Tumor Detection and Classification Using GoogleNet Algo...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Dharmaraj, N. Suresh Kumar, A. Lakshmikanth, J. Charulatha, R.T. Murugasami, R. Amarnath, M.A. Department of ECE Rajalakshmi Institute of Technology Chennai India Department of ECE M.I.E.T. Engineering College Trichy India Department of CSBS Rajalakshmi Institute of Technology Chennai India Nandha Engineering College Erode India JJ College of Engineering and Technology Trichy India

ISBN: (纸本)9789819613472

Tumors are aberrant tissue growths that can develop in any body organ. Numerous varieties of human tumors have been discovered in recent years, including brain, bone, and lung. image processing is essential in tumor analysis and categorization. Medical image processing is an essential field of study since the results are utilized to improve health issues. When cells within the bone divide uncontrollably, they produce lumps or lumps of aberrant tissue. There are numerous types of bone tumors, each with its own set of characteristics. Bone tumors are classified as noncancerous (benign) or cancerous (malignant). Our project primarily focuses on picture segmentation and classification of skeletal images. Initially, the Fast Non-Native Media (FNLM) filter is used for preprocessing. For the segmentation procedure, a sophisticated algorithm is employed to distinguish malignant nodules from lung pictures. In feature extraction, distinct features are extracted using a gray-level co-occurrence matrix (GLCM). The GoogleNet ranking algorithm is then used to rank the specified features. The suggested classifier is capable of detecting bone cancer with high accuracy. The proposed approach was built in MATLAB, utilizing the dataset from the Bone image database. Various performance measures are assessed and connected with existing classifiers and cutting-edge algorithms. Simulation results show that the devised scheme has a high classification accuracy (99%) compared to previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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