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

共 10244 条记录，以下是71-80 订阅

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

You’re not acting like yourself: Deepfake Detection Based o...

引用

International image processing, applications and Systems conference (IPAS)

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

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

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.

关键词： Training Deepfakes image recognition Forensics Government Detectors Pressing Media National security Video recording

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

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

引用

International image processing, applications and Systems conference (IPAS)

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

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

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%.

关键词： Location awareness Accuracy image color analysis Authentication Watermarking Transforms Gray-scale Discrete wavelet transforms Cryptography Biomedical imaging

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Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

Let There Be Light: Robust Lensless Imaging Under External I...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Eric Bezzam Stefan Peters Martin Vetterli Audiovisual Communications Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interference (other sources, noise, etc.). In this work, we address a prevalent noise source that has not been studied for lensless imaging: external illumination e.g. from ambient and direct lighting. Being robust to a variety of lighting conditions would increase the practicality and adoption of lensless imaging. To this end, we propose multiple recovery approaches that account for external illumination by incorporating its estimate into the image recovery process. At the core is a physics-based reconstruction that combines learnable image recovery and denoisers, all of whose parameters are trained using experimentally gathered data. Compared to standard reconstruction methods, our approach yields significant qualitative and quantitative improvements. We open-source our implementations and a 25K dataset of measurements under multiple lighting conditions.

关键词： Source separation Noise Lighting Interference Reconstruction algorithms Cameras Optics Speech processing image reconstruction Standards

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applications of digital image processing xix

Applications of Digital Image Processing XIX

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applications of digital image processing xix 1996

The proceedings contain 74 papers. The topics discussed include: mosaic construction, processing, and review of very large electron micrograph composites;software organization for a prolog-based prototyping system for machine vision;parallel processing system based on LAN for image processing applications;structured region growing and recognition algorithm for nondestructive evaluation;rotational information in shape description;optimal window size for visual tracking;and probabilistic anti-aliasing methods for dynamic variable resolution images.

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

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

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

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.

关键词： image edge detection Computed tomography Software algorithms Signal processing algorithms Computer architecture Table lookup IP networks Logic Field programmable gate arrays Kidney

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

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

引用

Computational Intelligence, Communication Technology and Networking (CICTN), International conference on

作者： Shweta Singh Sudeep Varshney Ankur Chaudhary Department of CSE Sharda School of Engineering & Technology Sharda University Greater Noida AI/ML Computational Science Manager Accenture Pvt Ltd

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

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.

关键词： Surveys Location awareness image segmentation Text analysis image analysis Reviews digital images Layout Optical character recognition Feature extraction

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A Parameter-Free Normal Estimator on digital Surfaces 4th

A Parameter-Free Normal Estimator on Digital Surfaces

引用

4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Marêché, Aude Debled-Rennesson, Isabelle Feschet, Fabien Ngo, Phuc Université de Lorraine CNRS LORIA Nancy54000 France Université Clermont Auvergne CNRS ENSMSE LIMOS Clermont-Ferrand63000 France

ISBN: (纸本)9783031821493

The processing of 3D digital objects often requires the computation and analysis of their geometrical features. The normal vectors of the object’s surface in particular provide important information used in image processing applications. We present in this paper a new method for the estimation of normal vectors on the surface of a 3D digital object. It is both local and parameter-free. The proposed method involves the study of neighborhoods around points using planar sectors. Experimental evaluations using multigrid approaches show that it is both faster and more robust than state-of-the-art methods in the field, while being of comparable accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Geometry

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Development of a multi-modality based approach for Plastic Waste Segregation

Development of a multi-modality based approach for Plastic W...

引用

International image processing, applications and Systems conference (IPAS)

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

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Plastic waste is becoming nowadays a huge environmental problem. Circular economy seems to be the best choice for preserving the environment, by reinserting plastic waste into the industrial process and use. A key step for that is the plastic waste segregation from other waste. This paper proposes a new plastic waste segregation approach based on two modalities: spectroscopy and optical images. The research handles an optical camera for waste image acquisition, while the ground truth for plastic type identification, with respect to the seven primary plastic types: PET, HDPE, LDPE, PP, PS, PVC, and Other, is established through spectroscopic analysis. Various deep learning models were trained and evaluated on a dataset of publicly available and custom-collected images, aiming to achieve human-level accuracy in distinguishing between plastic types. The proposed methodology is based on a set of steps, where the model is first built implicitly on a segregation of different wastes, and then applied via transfer learning for separating the seven plastic types. The system performances demonstrate the feasibility and effectiveness of this combined approach for accurate and efficient plastic waste sorting. The proposed system has been implemented on Raspberry Pi 4 Model B+ as the central processing unit, integrating computer vision and deep learning techniques for efficient and accurate sorting.

关键词： Deep learning Waste management Visualization Computer vision Accuracy Embedded systems Biological system modeling Optical imaging Plastic waste Sorting

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A New Hybrid Model for Brain Tumor Recognition in MRI images Based on Hand-Crafted Features and Deep Learning

A New Hybrid Model for Brain Tumor Recognition in MRI Images...

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digital Signal processing and its applications (DSPA), International conference on

作者： Yasser Nizamli Weaam Fadel Anton Filatov Yulia Shichkina Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation Department of Computer Science Saint Petersburg Electrotechnical University Saint Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Brain tumors are among the most life-threatening cancers that can affect people of any gender or age. MRI scans manually evaluated by an expert are the initial diagnostic procedure that precedes an appropriate treatment plan. However, the human factor involves the possibility of error, and the process itself can be time-consuming and resourceconsuming, ultimately putting patients’ lives at risk. Therefore, efforts are being made to automate the diagnostic process through computer-aided systems, of which artificial intelligence has become the basis. This paper presents a novel system for detecting brain abnormalities in MRI images that ensures high precision without compromising the simplicity of its operational structure. In the proposed model, numerical features are extracted from grayscale MRI images using the rectangular histogram of oriented gradients method. The resulting features are fed after dimensionality reduction to a specialized sequential deep network composed of 11 learnable layers. The classification accuracy of the model reached 99.51%, outperforming many analogues, making it a promising option for supporting the diagnostic decision-making process.

关键词： Deep learning Histograms image recognition Magnetic resonance imaging Decision making Brain tumors Feature extraction Brain modeling Numerical models Testing

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Analysis of the durability damage scenarios of air spring sleeves with axial reinforcements based on computer tomography and digital image processing 13th

Analysis of the durability damage scenarios of air spring sl...

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13th European conference on Constitutive Models for Rubber

作者： Klauke, R. Charrier, P. Serrurier, N. Le Saux, M. Skoglund, M. Vibracoust ZI Carquefou France ESM Adv Engn Carquefou France ENSTA Paris Inst Sci Mecan & Applicat Ind IMSIA Paris France ENSTA Bretagne CNRS UMR IRDL Brest France Helmut Schmidt Univ Hamburg Hamburg Germany

ISBN: (纸本)9781032851648;9781003516880;9781032851389

Air springs are flexible components used in various applications such as vehicles, machinery, and industrial equipment. They are made of a multilayer composite material based on rubber and reinforcing textile cords. The service life and reliability of air springs depend heavily on their fatigue behaviour, which is influenced by various factors such as temperature, pressure, load and environmental conditions. To better understand and model the fatigue behaviour of air spring sleeves, it is necessary to identify and quantify the damage mechanisms and processes inside the individual layers. In this work, a new tool is presented that is able to distinguish the components of an air spring sleeve using volumetric CT-data. The tool is based on advanced greyscale detection technology to recognize and segment the different layers and materials in the data of a scanned sleeve. It allows to determine the geometry, thickness, and volume fractions of each layer and to detect potential defects such as cracks, delaminations, or holes. The tool is applied to various damage scenarios for the fatigue behaviour of air springs. The focus is put on fatigued axial sleeves subjected to cyclic loading. Various CT scan data sets of axial sleeves are analysed and compared to their initial state after being exposed to different load cases. It is shown how the tool helps to track the damage evolution in each layer and how to identify the critical areas. This paper provides the first results of the damage scenarios for fatigued axial sleeves. The presented tool will allow an in-depth analysis of the fatigue behaviour of axial air spring composite materials in future and can also be applied to other types of air spring sleeves or similar structures. It can provide an important contribution to improving the service life prediction and design of air springs.

关键词： Automobile springs

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