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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是21-30 订阅

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Text recognition in Persian Documents Using Self-Supervised Learning 6

Text Recognition in Persian Documents Using Self-Supervised ...

引用

6th ieee International conference on image processing. Applications and Systems, IPAS 2025

作者： Babaei, Atefeh Dehghani, Alireza Yazdi, Mehran Jamshidi, Ali Rasouli, Mahdi Shiraz University School of Electrical and Computer Engineering Shiraz Iran CeADAR Ireland's Centre For AI Dublin Ireland Université Grenoble Alpes School of Computer Science and Mathematics Grenoble France

ISBN: (纸本)9798331506520

Text is one of humankind's most significant inventions essential for communication and collaboration in modern society. Extracting text from images, especially for languages with cursive and connected scripts like Persian, presents unique challenges due to issues such as font types, word structures, and the scarcity of labeled data. This paper addresses these challenges by implementing deep learning techniques, specifically self-supervised learning with convolutional and recurrent neural networks (CNNs and RNNs). Our approach successfully mitigates the data scarcity problem, achieving an accuracy of 92.9% on the prepared dataset. © 2025 ieee.

关键词： Self-supervised learning

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A Review on the Applications of Machine Learning and Deep Learning Algorithms for image recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing.restrictions. The latest image processing.techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 ieee.

关键词： Adversarial machine learning

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Diffusion-based Identity-Preserving Facial Privacy Protection

Diffusion-based Identity-Preserving Facial Privacy Protectio...

引用

2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Han, Dong Mohamed, Salaheldin Li, Yong Denzler, Joachim Huawei Munich Research Center Friedrich Schiller University Jena Jena Germany Huawei Munich Research Center Munich Germany Computer Vision Group Friedrich Schiller University Jena Jena Germany

ISBN: (纸本)9798350368741

The efficacy of facial recognition systems that utilize deep learning techniques has led to significant concerns over privacy, since they possess the capability to facilitate unauthorized monitoring of individuals in the digital realm. Current techniques for improving privacy are ineffective in producing "naturalistic" photographs that can safeguard facial features and fail to ensure privacy while maintaining an optimal user experience. We present an innovative text-agnostic method for protecting facial privacy. Our method depends on manipulating the sampling process of a pretrained diffusion model utilizing the guidance from a target image face together with the original image and face guidance in an adversarial manner to produce a protected face image. We preserve the original visual information from the input face image for identity preservation while extracting general embedding information from the target face image for soft facial attribute transfer. The output protected face image from our method has imperceptible facial changes with enhanced privacy protection against state-of-the-art (SOTA) face recognition (FR) systems. Our extensive studies have shown that the faces generated using our method have a higher level of black-box adaptability, resulting in an absolute improvement of 6.4% on CelebA-HQ compared to the current most effective SOTA facial privacy protection technique in the face verification task while maintaining high image fidelity. © 2025 ieee.

关键词： Diffusion Face recognition Facial Privacy

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OSR: Toward Developing Efficient Federated Learning-based Human Activity recognition using Optimal Server Representations

OSR: Toward Developing Efficient Federated Learning-based Hu...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Khazaei, Ensieh Taha, Bilal Esmaeilzehi, Alireza Hatzinakos, Dimitrios Department of Electrical and Computer Engineering University of Toronto ON Canada

ISBN: (纸本)9798350368741

Federated Learning (FL) is a privacy-preserving algorithm that enables multiple clients to collaboratively train a global model without sharing their local data. This learning algorithm is particularly valuable in privacy-sensitive applications such as Human Activity recognition (HAR), where users are reluctant to share their personal data. However, a conventional FL system suffers from data heterogeneity and communication overhead. To address these issues, we propose an efficient FL algorithm for image-based HAR using optimal server representations (OSR). OSR efficiently selects a representative set of privacy-preserved images for transmission to the server and improves the global model quality by training on privacy-preserved data. Our comprehensive experiments carried out on three public datasets, namely Stanford40, PPMI, and VOC2012, demonstrate the superiority of OSR in terms of performance and bandwidth usage compared to state-of-the-art approaches. © 2025 ieee.

关键词： Federated Learning Human activity recognition Representation Learning

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Multi-label recognition under Noisy Supervision: A Confusion Mixture Modeling Approach

Multi-label Recognition under Noisy Supervision: A Confusion...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Gonzalez, Diego Linares Ibrahim, Shahana Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States

ISBN: (纸本)9798350368741

Multi-label recognition is a critical task in artificial intelligence, aiming to identify every object present in an image. Designing a multi-label classifier is a nontrivial task both from data collection and modeling perspectives. Collecting multiple labels for each image is extremely time-consuming and costly, which often leads to noisy annotations. Furthermore, a robust classifier that performs reliably well in the presence of such noisy labels demands meticulous modeling and learning criterion design. In this work, we propose a novel probabilistic confusion model that effectively incorporates inter-label interactions in causing label noise. The proposed model incorporates a latent variable, building a hierarchical structure to the label noise generation, and represents the label noise as a mixture of confusions caused by various classes. Under the proposed multi-label confusion mixture (MCM) model, we design an end-to-end learning criterion along with a sparsity regularization, that effectively estimates the true multi-label classifier. Experiments with various real-world datasets showcase the effectiveness of our approach. © 2025 ieee.

关键词： confusion model label noise multi-label learning sparsity

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

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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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Unsupervised image-to-image Style Transfer via Dual-Condition Diffusion Models

Unsupervised Image-to-Image Style Transfer via Dual-Conditio...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Fan, Anfei Yang, Jun Li, Wei Zhang, Chiyu College of Computer Science Sichuan Normal University Chengdu China

ISBN: (纸本)9798350368741

Style transfer is an artistic research topic within a series of generative tasks, and it is quite challenging to stably and reliably guide the generation of images with the expected content and style. Early methods followed a predefined style, defined by a reference image, and applied it to another image while preserving the latter's content structure, but the generated effects lacked high fidelity. As diffusion models have evolved, the content guidance for style transfer has transitioned from images to text, potentially leading to instance alterations. Generating with multiple conditions constrained in the latent space is challenging. Within the EDM framework, we have utilized cross-attention to design style and content embedding modules, overcoming instance alterations without the need for paired datasets and achieving high-resolution, high-fidelity results. Our experimental findings demonstrate that our architecture has advanced to the forefront of image-to-image style transfer capabilities. © 2025 ieee.

关键词： diffusion models image generation image-to-image style transfer

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Machine learning based fingerprint pattern recognition with Mach-Zehnder Interferometer

Machine learning based fingerprint pattern recognition with ...

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2024 International conference on Photonics Solutions, ICPS 2024

作者： Usman, Abdullahi Khun-In, Ravivudh Bhatranand, Apichai Chayawan, Chirasil Muhammad, Khalid Sabo Buranasiri, Prathan Department of Physics with Electronics Federal University P.M.B 1157Kebbi State Birnin Kebbi Nigeria Department of Physics Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok10140 Thailand Artificial Intelligence Photonics Advanced Research Laboratory Department of Physics School of Science King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand Electronic and Optoelectronic Device Research Unit School of Science King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand

ISBN: (数字)9781510688308

ISBN: (纸本)9781510688292

Fingerprints serve as an excellent means of individual identification. This study employed transmission-mode image detection using a Mach-Zehnder interferometer to produce two-dimensional fingerprint images. The research aims to develop a fingerprint pattern recognition system by integrating novel algorithms for ridge orientation analysis. The fingerprint patterns considered in this study include loops, arches, and whorls. The objective is to achieve high-resolution fingerprint patterns to analyze fingerprint variability, distinguish foreground from background regions, and trace ridge line orientations. The recognition algorithm is implemented using image processing.techniques, with a preprocessing.workflow that includes binarization, resizing, and histogram equalization for image enhancement. After preprocessing. the image undergoes ridge orientation analysis using the gradient method, and both similarity scores and pattern categorization were successfully achieved. The algorithm's performance was evaluated through MATLAB simulations, with results demonstrating the proposed system's accuracy and reliability across various fingerprint samples. The interferometric setup was also adapted to recognize microorganisms within a microfluidic channel, highlighting its potential applications in biometrics, forensics, and medical diagnostics. © 2025 SPIE.

关键词： Mach-Zehnder interferometers

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PsSR: Hybrid Path Selection Mechanism for Efficient image Super-Resolution

PsSR: Hybrid Path Selection Mechanism for Efficient Image Su...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Ding, Cheng Zhao, ZhongQiu Zhao, Yang Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350368741

In practical applications, large images are processed in patches, many of which are simple and smooth, making them suitable for lighter network processing. This paper proposes a hybrid path selection mechanism that enables the dynamic routing of patches through the network, achieving a balance between accuracy and computational efficiency. The proposed method develops a dual-branch architecture, in which the two branches keep the same structural design, but differ in the number of channels. An efficient classification module is integrated for directing patches to the appropriate branches within the model. In addition, within each branch, we introduce an early exit strategy to further reduce the calculation cost. Subsequently, we enhance the restoration capabilities of the lightweight branch through channel and spatial feature distillation. Experimental results demonstrate that our Hybrid Path Selection based Super-Resolution (PsSR) mechanism effectively reduces computational costs while preserving the integrity of the backbone performance. © 2025 ieee.

关键词： dual-branch knowledge distillation path selection super-resolution

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MSRFormer: Hybrid Scale Self-Attention and Local Fast Convolution Transformer for Facial Expression recognition

MSRFormer: Hybrid Scale Self-Attention and Local Fast Convol...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Shen, Zi-Qiang Tang, Yu-Yi Yan, Jun-Feng Li, Yang Zhao, Guo-Ying Hunan University of Chinese Medicine China Hunan Digital Chinese Medicine Engineering Technology Research Center China Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland

ISBN: (纸本)9798350368741

With the emergence of the Transformer architecture, the accuracy of deep learning within the domain of facial emotion recognition has seen further enhancement. However, Transformer comes with increased training complexity and time due to the large parameter count. Additionally, the global receptive field in Transformer's attention leads to unnecessary computations for features with limited spatial extent in image sentiment analysis. In this paper we presents a MSRFormer model, which combines Hybrid-scale self-attention and local fast convolution to address existing issues. The Hybrid-scale self-attention enables precise focus on salient regions of the image, where key features are located. A Local Fast Convolution module was integrated into the MLP head of the Transformer model, enhancing training speed and reducing parameters while maintaining feature learning. we conducted experiments on FERPlus, RAF-DB, and KDEF datasets and confirming its strong performance and broad applicability in facial expression recognition. © 2025 ieee.

关键词： facial expression recognition hybrid scale self attention local fast convolution transformer

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