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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4811 条记录，以下是1-10 订阅

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2023 ieee international conference on image processing and computer applications, icipca 2023

2023 IEEE International Conference on Image Processing and C...

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2023 ieee international conference on image processing and computer applications, icipca 2023

ISBN: (纸本)9798350314670

The proceedings contain 346 papers. The topics discussed include: research on anomaly test data processing method based on wavelet analysis;a sequential recommendation model combining contrastive learning and self-attention;research on passive UAV localization model based on topology-Monte Carlo coupling algorithm;wind-storage combined independent pumping power system control strategy;research on network attack vulnerability prediction based on neural network model;fine-grained sentiment analysis with a fine-tuned BERT and an improved pre-training BERT;transient temperature prediction of three-phase gas insulated busbar contacts based on CNN-LSTM;research on intelligent furniture system based on computer virtual reality technology;a robust log parsing algorithm — practice of Logslaw in heterogeneous logs of pacific credit card center of bank of communications(PCCC);inverse synthetic aperture radar image target recognition based on transfer learning;and lung cancer prediction based on learning machine.

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2024 ieee 2nd international conference on image processing and computer applications, icipca 2024

2024 IEEE 2nd International Conference on Image Processing a...

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2nd ieee international conference on image processing and computer applications, icipca 2024

ISBN: (纸本)9798350360240

The proceedings contain 376 papers. The topics discussed include: analysis of badminton motion trajectory algorithm based on neural network;intelligent insurance actuarial model under machine learning and data mining;research on real-time data transmission and signal processing system of CIM practical training teaching platform based on 5G network;design of systematic financial risk warning system based on integrated classification algorithm;development of a multi-objective optimization framework for submersible localization and search operations;improved thermal dome structure optimization design based on neural network algorithm;transmission line inspection image intelligent diagnosis system;and application of computer artificial intelligence infrared image processing technology in strength detection of building steel structure.

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2023 ieee international conference on image processing, ICIP 2023 - Proceedings

2023 IEEE International Conference on Image Processing, ICIP...

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30th ieee international conference on image processing, ICIP 2023

ISBN: (纸本)9781728198354

The proceedings contain 705 papers. The topics discussed include: GAITMM: multi-granularity motion sequence learning for gait recognition;IKD+: reliable low complexity deep models for retinopathy classification;scapegoat generation for privacy protection from deepfake;detecting stable diffusion generated images using frequency artifacts: a case study on Disney-style art;can we distill knowledge from powerful teachers directly?;NERD: neural field-based demosaicking;self-supervised focus measure fusing for depth estimation from computer-generated holograms;contour-assisted long-range perceptual network for camouflaged instance segmentation;and global-local awareness network for image super-resolution.

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Attention-Based image Caption Generation 5th

Attention-Based Image Caption Generation

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

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： image Caption computer Vision Natural Language processing Attention Mechanism Performance

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image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

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8th international conference on Arabic Language processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： Machine Learning Deep Learning computer Vision Generative Adversarial Networks Text-to-image Generation Arabic Text processing

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Simulation Verification Method for image Sensor Interface processing Unit Based on UVM 5

Simulation Verification Method for Image Sensor Interface Pr...

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5th ieee international conference on Power, Electronics and computer applications, ICPECA 2025

作者： Ma, Wenji Beijing Sunwise Information Technology Ltd. Department of FPGA Verification Organization Beijing China

ISBN: (纸本)9798331533694

image sensors are widely employed across various industries, including smart mobile devices, autonomous vehicles, and surveillance systems. The image Sensor Interface processing Unit is typically characterized by a high degree of complexity to support diverse image sensor interfaces. Consequently, the effective and efficient verification of this unit is essential for upholding project quality and adhering to timelines. This article presents a simulation verification methodology for the image Sensor Interface processing Unit utilizing the Universal Verification Methodology (UVM). It explores the features of different sensor interfaces, the architectural framework, core base classes, runtime mechanisms, and reusable components within the UVM verification platform. The study identifies functional verification points based on the specific characteristics and application contexts of the image Sensor Interface processing Unit and formulates testing incentives. By analyzing the simulation results, the approach ensures comprehensive coverage of all functional points, achieving a 100% functional coverage rate. The testing platform is systematically designed to provide robust portability while meeting fundamental functional verification requirements, thereby serving as a reference for the development of future module verification environments. © 2025 ieee.

关键词： Reusability

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Deep Learning Model Development for an Automatic Healthcare Edge Computing Application 5th

Deep Learning Model Development for an Automatic Healthcare ...

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

作者： Talal, Hadi Khamis, Ruaa Ali Al-Frady, Laith Middle Tech Univ Baghdad Iraq

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning (DL) is assisting academicians and medical professionals in uncovering latent opportunities in data and enhancing the healthcare industry. The edge computing applications like smart healthcare systems where accurate decision-making is required for fast medical treatment. DL in healthcare allows clinicians to correctly analyze any ailment and treat it, resulting in improved medical decisions. We present a unique DL model for the autonomous healthcare edge computing application in this paper. computer Aided Diagnosis (CAD) is an essential requirement of healthcare edge computing where the patient's medical data is used for fast and accurate disease prediction. Propose the DL-based CAD model for automatic disease classification from the input medical images. The model consists of pre-processing, DL-based feature engineering, and classification. Input medical image is first pre-processed for quality improvement and then automatic features are extracted using the pre-trained DL models (ResNet50 and Densenet201). The pre-trained models are improved by performing the feature scaling followed by a separate classification phase. The proposed CAD model is experimentally evaluated using the medical images dataset. The results reveal the efficiency of the proposed model compared to underlying solutions.

关键词： Automatic classification deep learning edge computing features engineering healthcare image processing

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image Integrity Checking Using Watermarking in Cloud Computing: A Review 3rd

Image Integrity Checking Using Watermarking in Cloud Computi...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Rani, Jyoti Nath, Rajender Department of Computer Science and Applications Kurukshetra University Kurukshetra Thanesar India

ISBN: (纸本)9789819726707

In today’s era of cloud computing, modification and tampering of digital images on cloud storage have turn out to be easier due to proliferation of digital image processing tools. Consequently, tamper detection and integrity checking of images on remote server emerged as huge concern. image watermarking techniques provide an efficient way to tackle such problems. This paper provides a systematic examination of existing watermarking methods currently in use in cloud computing environment. This paper also presents a comparative analysis of those techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Performance Comparison of Different Digital image Filters Used for Biomedical Signals 8th

Performance Comparison of Different Digital Image Filters U...

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8th international conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Sudagani Jyothi Muthu Krishnammal, P. School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819757855

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. MATLAB is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like mean, median, weighted average, Gaussian, and bilateral in Python to verify their performance in terms of mean square error (MSE) and similarity index (SI), a suitable filter can be selected for biomedical applications by comparing their performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

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

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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