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

共 16347 条记录，以下是271-280 订阅

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Application of Computer Vision for Efficient Maintenance of Cleanliness and Order in Commercial Areas: An Invention Approach for Automated Management in Commercial Zones 8

Application of Computer Vision for Efficient Maintenance of ...

引用

8th International conference on Communication and Information systems, ICCIS 2024

作者： Llacta, Jesus David Villon Simeon, Wiler Yosef Hurtado Rosales, Jorge Martin Vega Universidad Continental Facultad de Ingeniería Huancayo Peru Universidad Continental Faculty of Engineering Huancayo Peru

ISBN: (纸本)9798331531171

This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing commercial spaces, this paper proposes a technological model that utilizes strategically placed cameras and image processing algorithms to detect disorganized or dirty tables, sending real-time alerts to cleaning staff via a mobile application. The goal is to optimize resource use, reduce response times, and enhance customer experience. To validate the model, an experiment was conducted with a sample of 30 customers, measuring their satisfaction levels before and after the system's implementation. The results, analyzed through statistical tests, show a significant improvement in customer satisfaction. Additionally, the study discusses its limitations and proposes future research directions, including an evaluation of the costs associated with the automated system compared to traditional methods. © 2024 IEEE.

关键词： automation cleaning management commercial areas Computer vision customer experience detection algorithms image processing operational efficiency

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Optimization of Apriori Algorithm Based on Binary Coding and Matrix processing 7

Optimization of Apriori Algorithm Based on Binary Coding and...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Han, xinxin Wang, Jiao xiong, Xueguan Wang, Liuling Yang, xi Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350350760

—This research delves into the classic Apriori algorithm, comprehensively analyzing its concepts, properties, and existing limitations, and provides a detailed demonstration of the algorithm's execution flow through a case study. Aiming at the problems of traditional Apriori algorithms, such as low execution efficiency, heavy I/O, and excessive computation, an enhancement algorithm is proposed: By binary coding, the transaction sets of different items and adopting the storage structure of matrix processing data, Logical AND operations are used to enhance the execution efficiency of the algorithm, it is essential to minimize the number of database scans required during the process of identifying frequent item sets. This practical and efficient optimization algorithm offers hope for improved data mining processes. ©2024 IEEE.

关键词： Apriori algorithm Correlation rules Enhancement algorithm

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Integrated multi-branch neural network dehazing algorithm 6

Integrated multi-branch neural network dehazing algorithm

引用

6th International conference on Big Data and Artificial Intelligence, BDAI 2023

作者： Wang, Mei Li, Yunhong Su, Xueping Zhang, Lijian Wei, xiaoshuang Zhang, Fuxing Xi'an Polytechnic University School of Electronics and Information Xi'an China

ISBN: (纸本)9798350303667

Recently, the field of image dehazing has achieved rapid development. End-to-end deep learning algorithms achieve good performance. However, we observe that many algorithms perform poorly on the task of image dehazing in high-resolution complex environments. In this paper, aiming at the problems of detail loss, edge blurring and regional haze legacy in the process of dehazing non-uniform haze images with high resolution by existing algorithms, an end-to-end dehazing model integrating multiple neural networks is proposed. To be specific, we introduce a multiple neural network to deal with the aforementioned problems. Our proposed structure consists of three branches: the encoder-decoder network is constructed by transfer learning to reconstruct haze-free features;Full-resolution residual extraction network helps to avoid loss of details in downsampling layers;Swin-transformer scale extraction network makes the network receptive field richer. Then, their different features are then mapped through a learnable fusion tail. Extensive experimental results on real datasets show that our proposed ensemble multi-branch dehazing model has better recovery ability and good generalization ability on multiple datasets. © 2023 IEEE.

关键词： Learning algorithms

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Fundus image Classification for Glaucoma using U-Net Architecture and Logistic Regression 2

Fundus Image Classification for Glaucoma using U-Net Archite...

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2nd International conference on Paradigm Shifts in Communications Embedded systems, Machine Learning and Signal processing, PCEMS 2023

作者： Bajaj, Vanshika Kotambkar Shelke, Deepali M. Shri Ramdeobaba College of Engineering and Management Dept. of Information Technology Nagpur India

ISBN: (纸本)9798350310719

The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualified medical personnel, and takes less time in order to address this fundamental issue. A Computer-Aided Diagnosis (CAD) system, which employs different algorithms for medical image processing and analysis, can assist in achieving this. One of the ways to diagnose glaucoma is to calculate Optic Cup to Optic Disc ratio (CDR) and this can be done with the help of CAD algorithms. In medical image processing the primary focus is on image segmentationand its classification in order to obtain a result. In this paper, the exploration the best-known CNN model, U-Net for image segmentation of Optic Disc and Optic Cup from a fundus image and Logistic Regression, a classification model to determine a relationship between these two terms rather than previously used CDR formulas. © 2023 IEEE.

关键词： Computer aided diagnosis

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Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for image Editing 38

Schedule Your Edit: A Simple yet Effective Diffusion Noise S...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Lin, Haonan Chen, Yan Wang, Jiahao An, Wenbin Wang, Mengmeng Tian, Feng Liu, Yong Dai, Guang Wang, Jingdong Wang, Qianying School of Comp. Science & Technology MOEKLINNS Lab Xi'an Jiaotong University China College of Comp. Science & Technology Zhejiang University of Technology China School of Auto. Science & Engineering MOEKLINNS Lab Xi'an Jiaotong University China Institute of Cyber-Systems and Control Zhejiang University China SGIT AI Lab State Grid Corporation of China China Baidu Inc. China Lenovo Research China

Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires inverting the source image into a latent space, a process often hindered by prediction errors inherent in DDIM inversion. These errors accumulate during the diffusion process, resulting in inferior content preservation and edit fidelity, especially with conditional inputs. We address these challenges by investigating the primary contributors to error accumulation in DDIM inversion and identify the singularity problem in traditional noise schedules as a key issue. To resolve this, we introduce the Logistic Schedule, a novel noise schedule designed to eliminate singularities, improve inversion stability, and provide a better noise space for image editing. This schedule reduces noise prediction errors, enabling more faithful editing that preserves the original content of the source image. Our approach requires no additional retraining and is compatible with various existing editing methods. Experiments across eight editing tasks demonstrate the Logistic Schedule's superior performance in content preservation and edit fidelity compared to traditional noise schedules, highlighting its adaptability and effectiveness. (Project page: https://***/SYE/). © 2024 Neural information processing systems foundation. All rights reserved.

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Breaking Through Color Casts: Enhancing image Fidelity with Machine Learning-Based Correction

Breaking Through Color Casts: Enhancing Image Fidelity with ...

引用

2024 International conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Reddy, Choppa Jeevan Sai Akshay, Bavani Bhawane, Bange Reddy, Busireddy Parvathammagari Srikanth Satish, Addanki Sree Vidyanikethan Engineering College Tirupati India School of Engineering Mohan Babu University Tirupati Dept. of Ece India

ISBN: (纸本)9798350375237

L Color cast, an aberration common in digital images, poses challenges in various image processing applications, affecting image quality and visual perception. This research investigates diverse methodologies for color cast correction, ranging from traditional algorithms to modern machine learning-based approaches. Leveraging a comprehensive dataset of original and corrected images, the present study evaluates the efficacy of each method using quantitative metrics, including ACMO, BREN, GRAS, LAPM, LAPV, LAPD, and WAVV. Results indicate that while traditional techniques like Gray World Algorithm and White Patch Retinex Algorithm demonstrate moderate effectiveness, the implemented machine learning-based algorithm showcases superior performance across multiple color cast levels. By employing linear regression on RGB values, the method efficiently corrects color cast aberrations, yielding visually appealing and perceptually accurate results. Furthermore, the research highlights the significance of robust color constancy algorithms and their role in mitigating color cast distortions in digital images. This study contributes valuable insights into the field of color cast correction, offering practitioners in image processing and computer vision a comprehensive understanding of effective correction strategies. Future research directions may explore advanced machine learning models and integration with color constancy mechanisms to further enhance color cast correction techniques. © 2024 IEEE.

关键词： image enhancement

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A text-driven image style transfer model based on CLIP and SCBAM 3

A text-driven image style transfer model based on CLIP and S...

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3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Wu, Haodong Geng, Guohua Zhao, Yanting Wang, xiaolei Li, Qihang School of Information Science and Technology Northwest University Xi'an710127 China

ISBN: (纸本)9781510668522

To address the issues of low image quality and inadequate detail features encountered in current zero-shot style transfer algorithms, we propose a new text-driven image style transfer model. The model first uses CLIP (Contrastive Languageimage Pre-Training) model to convey the semantic information of text conditions. In addition, We designed a lightweight network, which can quickly express texture information according to text conditions, minimize the similarity cosine distance between the transferred image and the text conditions by CLIP model, and finally obtain the style transfer image. Furthermore, we introduce dual attention mechanism, identity consistency loss, content and style feature loss to make the translated image more vivid and realistic. Extensive experimental results demonstrate that our approach enables the transfer of multiple styles based on text conditions, achieving a broader, more realistic, and faster style transfer compared to existing methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

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4th International conference on Intelligent systems and Pattern Recognition-ISPR

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Univ Sidi Mohamed Ben Abdellah Dept Math & Comp Sci M2PA Lab ENS Fes 30030 Morocco

ISBN: (纸本)9783031821523;9783031821530

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.

关键词： Deep Learning image processing image Denoising Convolutinal Neural Networks (CNNs) Blind Noise DnCNN DRNet RIDNET PSNR SSIM

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Design and Implementation of a Novel image Encryption System 8

Design and Implementation of a Novel Image Encryption System

引用

8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Chen, Enhao Shu, Yingying Chen, Zongwei Wang, xinru Nie, xin Gao, Shuyi Liu, Zongrui Zou, Yijia Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology Faculty of Materials Science and Engineering Harbin China Harbin Institute of Technology Faculty of Science Harbin China Harbin Institute of Technology Faculty of New Energy Harbin China Harbin Institute of Technology School of Economics and Management Harbin China

ISBN: (纸本)9798350302455

The field of image encryption is studied to encrypt and hide the image effectively, and the most critical thing is whether the image can be restored without any loss of details or distortion. Classical cryptography provides good encryption and decryption algorithms for one-dimensional data streams, encrypting plaintext data into ciphertext data for the purpose of confidentiality. Although we can consider the image data as a one-dimensional data stream and use traditional encryption algorithms for encryption, these algorithms often ignore some special properties of digital images such as two-dimensional self-similarity, large data volume, etc. Moreover, traditional encryption algorithms can hardly meet the real-time requirements in network transmission. Therefore, we improve the traditional encryption techniques for digital images, and process the images by means of Arnold matrix transform, two-dimensional logistic chaos mapping, and DWT and DCT transforms, and experimentally verify that the method has faster and lower loss degree, which confirms the feasibility of its application in the field of professional encryption. © 2023 IEEE.

关键词： Mapping

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Robust image watermarking scheme based on orientation of local image texture 16

Robust image watermarking scheme based on orientation of loc...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Li, Qiutong Zhang, Qiyuan Yuan, xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

With the rapid development of big data and Internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust watermarking technology can effectively resist various attacks and protect image copyright. Most of the existing robust watermarking schemes generally adopt the global embedding method and often ignore the texture orientation of the cover image itself leading to poor imperceptibility and robustness. In this paper, we propose an innovative digital image robust watermarking scheme. Firstly, we conduct the Oriented FAST and Rotated BRIEF (ORB) and Speeded Up Robust Features (SURF) feature point detection algorithms, combined with the texture complexity of the cover image to select the most complex texture region as the local embedding location of the watermark. Secondly, to further improve the robustness of the watermarked image, we conduct the Discrete Cosine Transform (DCT) adaptive inter-block correlation algorithm. We embed the watermark into the cover image by adjusting the difference between the image embedding block and the adjacent block. We have tested a dataset containing multiple images, in which various common attacks are simulated and multiple cover images are tested. By comparing the experimental results, our scheme performs better regarding robustness and imperceptibility. © 2025 SPIE.

关键词： Cosine transforms

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