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

共 10200 条记录，以下是11-20 订阅

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach 6

Low-Data Classification of Historical Music Manuscripts: A F...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Shatri, Elona Raymond, Daniel Fazekas, Gyorgy Queen Mary University of London Centre for Digital Music London United Kingdom Queen Mary University of London Big Data Science London United Kingdom

ISBN: (纸本)9798331506520

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques. © 2025 IEEE.

关键词： Self-supervised learning

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 IEEE.

关键词： Frequency domain analysis

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 IEEE.

关键词： image analysis

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management 6

Waste Sorting System Using AI: Development of An Embedded So...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Remili, Amal Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge. © 2025 IEEE.

关键词： AI models Computer vision Waste detection Waste management Waste sorting

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A Review On Fusion Of Spiking Neural Networks And Transformersc 6

A Review On Fusion Of Spiking Neural Networks And Transforme...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Marsi, Oumaima Ambellouis, Sebastien Mennesson, Jose Meurie, Cyril Fleury, Anthony Tatkeu, Charles Univ. Gustave Eiffel COSYS LEOST IMT Nord Europe Institut Mines-Télécom LilleF-59000 France Univ. Gustave Eiffel COSYS LEOST LilleF-59000 France Univ. Lille Centre for Digital Systems IMT Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798331506520

This paper provides a comprehensive review of the integration of Spiking Neural Networks (SNNs) and Transformers, combining the energy efficiency of SNNs with the high performance of Transformer architectures. By leveraging the event-driven nature of SNNs and the powerful self-attention mechanism of Transformers, this fusion aims to address the challenges of high energy consumption in deep learning while improving task accuracy, especially for complex datasets. We introduce the core concepts of SNNs and Transformers, reviewing state-of-the-art methods for their combination, including hybrid architectures. The performance of each architecture is presented thanks to both static and neuromorphic datasets, highlighting their advantages and limitations. This review also discusses the challenges of integrating self-attention into spiking architectures and outlines future research directions to further enhance model performance and energy efficiency. © 2025 IEEE.

关键词： Distribution transformers

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You're not acting like yourself: Deepfake Detection Based on Facial Behavior 6

You're not acting like yourself: Deepfake Detection Based on...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Libourel, Alexandre Dugelay, Jean-Luc Eurecom Digital Security Biot France

ISBN: (纸本)9798331506520

Politicians and government leaders are critical targets for deepfake attacks. A single deepfake involving these individuals can severely damage their careers or, in extreme cases, pose a national security threat. Attackers can leverage vast amounts of publicly available audio and video recordings to train their models, making this threat even more pressing. In response, specialized deepfake detectors have been developed to focus on detecting deepfakes targeting a specific Person of Interest (POI). By learning facial expressions and movements unique to the POI, these detectors can identify inconsistencies in deepfakes where these authentic attributes are absent. However, previous methods relied on Facial Action Units, which offer an incomplete representation of the POI's behavior. In this paper, we propose a novel approach to learning POI-specific movements without requiring deepfake samples during training, making it independent of any deepfake generation methods. Although our technique is speaker-dependent, it provides a robust solution for protecting high-profile individuals who are particularly exposed to deepfake threats. © 2025 IEEE.

关键词： National security

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Ma, Xinchen Lu, Yue School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloemxxxxc/TRIFORMER. © 2025 IEEE.

关键词： image restoration transformer Ultra-high-definition(UHD)

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Progressive Enhancement of Anatomical Structural and Medical Feature Learning for Cephalometric Landmark Detection 24

Progressive Enhancement of Anatomical Structural and Medical...

引用

7th International conference on digital Medicine and image processing, DMIP 2024

作者： Li, Mengqi Qi, Senrong Zhu, Xuemei Niu, Kai Han, Jianhui Li, Ying He, Zhiqiang Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Beijing Stomatological Hospital & School of Stomatology Capital Medical University Beijing China Beijing University of Posts and Telecommunications Hospital Beijing China

ISBN: (纸本)9798400709586

Landmark detection is an essential foundation of cephalometric analysis, playing a crucial role in clinical diagnosis and treatment of orthodontic and orthognathic conditions. This paper proposes a novel framework that enables targeted learning and extraction of structural and medical features based on neural network. Specifically, our framework's key components comprise Contour Sharpener, Coupling Transformer, and Progressively Targeted Feature Extraction Networks (PT-FENet). The Contour Sharpener intensifies contour features informed by the prior distributions of landmarks, enabling the network to learn anatomical structural features. The Coupling Transformer couples and transforms associated landmarks based on the similarity of features around medically related landmarks and the invariant structure of geometrically constrained landmarks to enhance the learning of anatomical structures and medical correlations among them. PT-FENet fully extracts multi-scale features and progressively targets the refinement of anatomical structural and medical features grounded in prior knowledge, thereby augmenting the attention towards detection information. Experimental results demonstrate that our method outperforms existing state-of-the-art methods on public dataset. Our method enables rapid batch processing of images for precise landmark detection, thus possessing significant value in clinical cephalometric analysis and orthodontic applications. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Batch data processing

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Comprehensive Analysis and Framework for Text and Graphics Separation in Document images 2

Comprehensive Analysis and Framework for Text and Graphics S...

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2nd International conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Singh, Shweta Varshney, Sudeep Chaudhary, Ankur Department of CSE Sharda School of Engineering & Technology Sharda University Greater Noida India Accenture Pvt Ltd. India

ISBN: (纸本)9798331530389

The process of breaking up a digital image into many parts is called segmentation. These sections in scanned papers, are those that have backgrounds, texts, and images. In applications linked to document analysis, text segmentation is a significant issue. The separation of a complex document into non-text and text components is a major challenge in document image analysis. Localization of text in printed document images is a critical processing step for page layout analysis (PLA), specifically for obtaining textual data. Several algorithms have been developed for this subject. However, many algorithms only produce accurate answers for a limited type of documents due to their reliance on specific attributes or assumptions. There is a need to develop effective techniques for many sorts of documents, such as newspapers, magazines, documents, and articles, with arbitrary layouts and nonhomogeneous backgrounds. This study examines the various ways that distinguish between non-text and text in document images. © 2025 IEEE.

关键词： image segmentation

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