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

共 12901 条记录，以下是261-270 订阅

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Performance Study of SIFT and Swin Transformer algorithms in Fuzzy Duplicate image Detection Problem 4th

Performance Study of SIFT and Swin Transformer Algorithms ...

引用

4th International conference on Novel and Intelligent Digital systems, NiDS 2024

作者： Bystryakov, Daniil V. Levshin, Denis V. Zubkov, Alexander V. Donsckaia, Anastasia R. Software Engineering Department Volgograd State Technical University Volgograd Region Russia

ISBN: (纸本)9783031733437

The quantity of data in any field of activity is increasing at an exponential rate on an annual basis. images are no exception to this rule. In such circumstances, the importance of image analysis and processing is becoming increasingly evident. One such task is the detection of fuzzy duplicates of images, which is of significance for search engines and databases, as well as for fields such as medicine, security and law enforcement, advertising and marketing, and so on. Despite the existing variety of fuzzy duplicate search algorithms and models, there is no unambiguous answer as to which approach will be the most effective for a particular problem. This study examines two approaches to fuzzy duplicate image retrieval: one based on SIFT descriptors and the other on Swin Transformer. The current work presents the experimental results of the two models. The study compares the two models in terms of recall, specificity, PPV (positive predictive value), NPV (negative predictive value) and accuracy. The Swin Transformer model demonstrated superior performance across all evaluated metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Marketing

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FashionR2R: Texture-preserving Rendered-to-Real image Translation with Diffusion Models 38

FashionR2R: Texture-preserving Rendered-to-Real Image Transl...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Hu, Rui He, Qian He, Gaofeng Zhuang, Jiedong Chen, Huang Liu, Huafeng Wang, Huamin Zhejiang University China Style3D Research China

Modeling and producing lifelike clothed human images has attracted researchers' attention from different areas for decades, with the complexity from highly articulated and structured content. Rendering algorithms decompose and simulate the imaging process of a camera, while are limited by the accuracy of modeled variables and the efficiency of computation. Generative models can produce impressively vivid human images, however still lacking in controllability and editability. This paper studies photorealism enhancement of rendered images, leveraging generative power from diffusion models on the controlled basis of rendering. We introduce a novel framework to translate rendered images into their realistic counterparts, which consists of two stages: Domain Knowledge Injection (DKI) and Realistic image Generation (RIG). In DKI, we adopt positive (real) domain finetuning and negative (rendered) domain embedding to inject knowledge into a pretrained Text-to-image (T2I) diffusion model. In RIG, we generate the realistic image corresponding to the input rendered image, with a Texture-preserving Attention Control (TAC) to preserve fine-grained clothing textures, exploiting the decoupled features encoded in the UNet structure. Additionally, we introduce SynFashion dataset, featuring high-quality digital clothing images with diverse textures. Extensive experimental results demonstrate the superiority and effectiveness of our method in rendered-to-real image translation. © 2024 Neural information processing systems foundation. All rights reserved.

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image Search Engine with Recognition 4

Image Search Engine with Recognition

引用

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

作者： Kumar, Jmsv Ravi Dheeraj, Bellaganti Gowtham, Geddada Babu, Dokala Vivek Kiran, Bonthu Asha Srikanth, M. Srkr Engineering College Department of Information Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9798331523923

The image Recognition Search Engine is one of the modern advances in artificial intelligence and image analysis and processing. This advanced system surpasses typical functionalities in image identification as it captures features to solve new problems faced in existing systems of digital media authentication, content designing, and improvement of images. The system core relies on advanced AI algorithms that can rasterically recognize and analyze various forms of image objects ranging from objects, faces, scenes, and brand logos converting what appears to be simple images into indexical, analyzable data. One feature is that the system can integrate deepfake detection technology, which is relevant in the context of increasing the amount of manipulated content in the media space. Using special algorithms to learn even the least detectable abnormality in images including the presence of unnatural distinctive facial characteristics and changes in lighting conditions, we obtain a highly effective tool for improving the reliability of image identification. Furthermore, the platform allows for new features to convert an image into a video by implementing frame interpolation and motion estimation through animating the images to allow users to create interesting clips for applications regarding marketing or any personal use. © 2025 IEEE.

关键词： Motion estimation

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A Novel Approach to image Restoration and image Enhancement 3rd

A Novel Approach to Image Restoration and Image Enhancement

引用

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

作者： Singh, Divya Upadhayay, Bhawna Gupta, Pradeep Gupta, Sonam Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819708918

image restoration and image enhancement are fundamental tasks in computer vision and image processing. image restoration aims to recover the original information from a degraded or corrupted image, while image enhancement aims to improve the visual quality and interpretability of an image. This research paper presents a comprehensive theoretical framework that integrates both image restoration and image enhancement techniques. We explore various methods, algorithms, and mathematical models to address the challenges associated with these tasks. The proposed framework leverages both classical and deep learning-based approaches to achieve superior performance in restoring and enhancing images. The experimental results demonstrate the effectiveness and versatility of our proposed approach in a wide range of applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image reconstruction

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ICCAI 2024 - Proceedings of the 2024 10th International conference on Computing and Artificial Intelligence

ICCAI 2024 - Proceedings of the 2024 10th International Conf...

引用

10th International conference on Computing and Artificial Intelligence, ICCAI 2024

ISBN: (纸本)9798400717055

The proceedings contain 72 papers. The topics discussed include: classification of degradation level of pineapple leaf spot disease using MobileNetV3;multi-processing depth map estimation for single-view 3D image generation;artistic outpainting through adaptive image-to-text and text-to-image generation;visual presentation learning with contrastive learning for radiology report generation;data-driven methodology for bike route identification to enhance urban cycling infrastructure in metropolitan manila;person re-identification from video surveillance systems using artificial intelligence methods;assessing the security posture of mobile automated fingerprint identification systems (MABIS): a comprehensive analysis for enhanced resilience;and facemasks detection with different orientation using deep learning algorithms.

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Review of Artificial Intelligence methods for detecting cancer in medical image processing 3

Review of Artificial Intelligence methods for detecting canc...

引用

3rd International conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sarkar, Priya Saha, Ashim National Institute of Technology Agaratala Computer Science and Engineering Agartala India

ISBN: (纸本)9781665462167

Cancer is the unchecked spread of aberrant cells throughout the body. Cancer is a general word for a set of diseases brought on by the growth of abnormal cells in various bodily parts. Lung cancer, breast cancer, skin cancer, oral cancer, colon cancer, and prostate cancer are just a few of the more than a hundred different forms of cancer. Delays in treatment might result in major health problems and even death. This research reviews strategies for detecting liver, brain, and lung cancer using image processing. Automated and computer-aided detection systems (CAD) with artificial intelligence are the approaches utilized for detection, and these systems are good at processing big datasets to produce accurate and effective results in the detection of cancer. To be compatible with AI, these processing systems must overcome numerous obstacles, including picture capture, pre-processing, data management, segmentation, and classification algorithms. The numerous picture acquisition and segmentation approaches are reviewed in this work. These methods are now essential to meet the needs of the expanding patient population and to improve the healthcare system. © 2023 IEEE.

关键词： Diseases

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Learning-based image Analytics in User-AI Agent Interactions for Cyber-enabled Manufacturing 4

Learning-based Image Analytics in User-AI Agent Interactions...

引用

4th IEEE International conference on Human-Machine systems, ICHMS 2024

作者： Alarcon, Mauro Lemus Roy, Upasana Kambhampati, Anirudh Nguyen, Nguyen Attari, Minasadat Surya, Ramakrishna Bunyak, Filiz Maschmann, Matthew Palaniappan, Kannappan Calyam, Prasad University Of Missouri Columbia United States

ISBN: (纸本)9798350315790

Manual trial-and-error methods are employed for image parameter selection decisions in the processes that control cyber-enabled scientific instruments. Particularly in materials manufacturing use cases, where image analytics can be iterative, time-consuming and prone to errors, there is a need to enhance existing processes by using agents featuring learning algorithms that recommend image analytics parameters for users. In this paper, we present an analysis focused on identifying the optimal learning algorithm for an AI agent guiding real-time image processing tasks in carbon nanotube (CNT) manufacturing, which involves enhancing instrument control of cyber-enabled scanning electron microscope (SEM) instrument image scanning setup parameters such as Zoom, Focus, and Contrast. Specifically, we demonstrate the use of Reinforcement Learning (RL) and Imitation Learning (IL) based agents within a Remote Instrumentation Science Environment (RISE), a modularized system capable of utilizing multiple learning algorithms for guiding image analytics tasks. Further, we conduct a comparative performance analysis of RL and IL using 236CNT images captured in SEM material synthesis experiments. The experiments include configuring scanning parameters for CNT images generated by SEM, implementing CNT image segmentation, and assessing the effectiveness of RL and IL agents in identifying scanning parameters to enhance image quality. The objective is to predict Zoom, Focus, and Contrast parameters using limited labeled data for offline training, guiding dynamic adjustments in SEM settings. Our findings reveal that the IL agent outperforms the RL agent under dynamic conditions in characterizing CNT image parameters - specifically, Zoom, Focus, and Contrast - by evaluating image segmentation metrics. © 2024 IEEE.

关键词： Iterative methods

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Color Medical image Encryption Based on Chaotic System and DNA 2nd

Color Medical Image Encryption Based on Chaotic System and D...

引用

3rd International conference on Digital Technologies and Applications

作者： Maloufy, Ahmed E. L. Karmouni, Hicham Tahiri, Mohamed Amine Qjidaa, Hassan Sayyouri, Mhamed Jamil, Mohamed Ouazzani Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Engn Syst & Applicat Lab Fes Morocco Cadi Ayyad Univ Natl Sch Appl Marrakech Morocco Sidi Mohamed Ben Abdellah Fez Univ CED ST STIC Lab Elect Signals & Syst Informat LESSIDhar El M Fes Morocco Univ Privee Fes UPF Lab Syst & Environm Durable SED Fac Sci Ingn FSI Fes Morocco

ISBN: (纸本)9783031298561;9783031298578

The discipline of information security known as deoxyribonucleic acid (DNA) cryptography is one of the newest and most promising areas. In this context, we suggest a new color medical imaging algorithm that combines the biogenetic principle of DNA with a four-dimensional, extremely chaotic system. This program encrypts the image blocks using the hyper-chaotic system's chaotic output, then determines the encoding, decoding, and DNA calculation of each image block to increase key space and resist image processing attacks. Simulation results exhibit the strength and efficiency of the suggested algorithm against different types of image processing attacks compared to other encryption algorithms.

关键词： Color image Encryption Decryption Chaotic systems DNA

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Automated Anemia Detection Through Fingernail image Analysis 4

Automated Anemia Detection Through Fingernail Image Analysis

引用

4th International conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Geetha, A. Stanley, Mini Anuradha, S. Maya, P.R. Nalini, M. Sriharipriyan, P. Sri Sairam Engineering College Department of English Chennai India Sri Sairam Engineering College Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331508845

Millions of people worldwide, especially in low- and middle-income nations, suffer from anemia, a common health problem. The disease is characterized by a lack of hemoglobin or red blood cells, which can result in symptoms including weakness and exhaustion as well as serious consequences if ignored. Conventional techniques for identifying anemia usually entail intrusive blood tests, which can be expensive, time-consuming, and sometimes unavailable to a large number of people. In order to overcome these obstacles, this study uses MATLAB to provide an automated, non-invasive method for detecting anemia via fingernail image analysis. This project's main goal is to develop an effective and precise anemia screening tool by utilizing machine learning and image processing methods. The fingernail photos are taken by the system and pre-processed to improve quality and guarantee consistency. The technology uses sophisticated image processing techniques to extract pertinent elements from the fingernail photos, including texture and color characteristics that indicate hemoglobin levels. Machine learning algorithms use these traits to categorize the photos as either anemic or non-anemic. MATLAB is used because of its strong skills in machine learning and image processing. From picture capture and pre-processing to feature extraction and classification, the program streamlines the whole procedure. MATLAB's extensive toolkit makes it possible to manipulate photos effectively and has strong machine learning capabilities, which makes it the perfect platform for creating an automatic detection system. The system is guaranteed to be scalable, simple to install, and accurate through the use of MATLAB. Clinical data is used to thoroughly validate the system's functionality. The machine learning models are trained and tested using images from both non-anemic individuals and patients with clinically confirmed anemia. The system's sensitivity, specificity, and accuracy are assessed to guar

关键词： Noninvasive medical procedures

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Intelligent processing algorithms for Motion Capture Data Oriented to Artificial Intelligence 2

Intelligent Processing Algorithms for Motion Capture Data Or...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Lu, Zhenglei Beijing Information Technology College General Ability Teaching Department Physical Education Teaching Research Office Beijing China

ISBN: (纸本)9798350317558

Traditional motion capture systems are prone to environmental interference, resulting in noise and errors in the captured data. This article proposes an artificial intelligence oriented intelligent processing algorithm for motion capture data, which improves the quality of motion capture data and reduces noise and errors. This article utilizes sensors to collect a large amount of motion timing data and motion image data, preprocesses the data, and constructs a Convolutional Neural Network (CNN) Long Short Term Memory (LSTM) model. By using the CNN model to extract features from images, this paper uses the LSTM model to analyze temporal data, fuses the output of LSTM and CNN, and finally estimates the motion attitude. The data results of the test set indicate that the average mean square error of motion attitude estimation using the CNN-LSTM model is only 0.03. The use of the CNN-LSTM model can effectively improve the performance of motion data pose analysis. © 2024 IEEE.

关键词： attitude estimation convolutional neural network intelligent processing long short term memory motion capture data

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