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

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Perceptual Fairness in image Restoration 38

Perceptual Fairness in Image Restoration

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ohayon, Guy Elad, Michael Michaeli, Tomer Faculty of Computer Science Technion-Israel Institute of Technology Israel Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology Israel

Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (e.g., women) only if it falls within the group's set of ground truth images (e.g., natural images of women);otherwise, it is considered entirely incorrect. Consequently, such definitions are prone to controversy, as errors in image restoration can manifest in various ways. In this work we offer an alternative approach towards fairness in image restoration, by considering the Group Perceptual Index (GPI), which we define as the statistical distance between the distribution of the group's ground truth images and the distribution of their reconstructions. We assess the fairness of an algorithm by comparing the GPI of different groups, and say that it achieves perfect Perceptual Fairness (PF) if the GPIs of all groups are identical. We motivate and theoretically study our new notion of fairness, draw its connection to previous ones, and demonstrate its utility on state-of-the-art face image restoration algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Research on Two-Dimensional Code Cable Equipment Information Extraction and Security Verification Technology Based on image processing Algorithm

Research on Two-Dimensional Code Cable Equipment Information...

引用

2024 International conference on Optical Communication and Optoelectronic Technology, OCOT 2024

作者： Chen, Shaoxin Xu, Mengran Bi, Weican Wei, Mianxin Guangdong Power Grid Co. Ltd. Guangzhou Huadu Power Supply Bureau Guangdong Guangzhou510800 China

ISBN: (纸本)9781510683266

This paper delves into the extraction and secure verification technology of cable equipment information based on image processing algorithms, demonstrating how the combination of high-resolution cameras and circular LED lights effectively improves image quality to adapt to complex industrial environments. The research applies Reed-Solomon error correction algorithms and other advanced decoding techniques to ensure accurate data recovery even in the case of damaged QR codes. The article also introduces the application of digital signatures and timestamps in enhancing data anti-counterfeiting and authenticity verification, greatly enhancing the security and accuracy of the system through rigorous data formatting and verification processes. The comprehensive use of these technologies significantly enhances the efficiency, security, and reliability of QR code systems in industrial applications, which is of great significance for promoting the intelligent and automated development of equipment management. © 2024 SPIE.

关键词： Reed-Solomon codes

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image Segmentation based Approach for Skin Disease Detection and Classification using Machine Learning algorithms 2

Image Segmentation based Approach for Skin Disease Detection...

引用

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

作者： Sany, Nazmul Hasan Chandra Shill, Pintu Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

Skin disorders are ubiquitous, stemming from causes including fungus, germs, allergies, and viruses. Although laser and photonics medical technologies have revolutionised rapid and precise diagnosis, their exorbitant prices restrict accessibility. To address this, this paper describes an image processing strategy for dermatological screening and procedure that comprises obtaining digital photographs of damaged skin patches and applying image processing to detect the disease type. Moreover, when conducted manually by medical specialists, identifying skin illnesses is time-intensive and subjective. As a consequence, both patients and physicians want automated skin disease prediction, which makes the treatment plan faster. This cost-effective approach depends on a basic setup: a camera and a computer. Operating on colour photos, a solution combines resizing for feature extraction using a pertained convolutional neural network, followed by classification using a multiclass SVM. The technology efficiently recognises four separate skin illnesses with an astonishing 90.8% accuracy, giving users complete findings spanning disease kind, extent, and severity. © 2024 IEEE.

关键词： Decision Tree image processing K-NN Machine Learning Skin Disorder SVM

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image to Text Conversion Using Deep Learning algorithms: Survey 10

Image to Text Conversion Using Deep Learning Algorithms: Sur...

引用

10th International conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Saranya, M. Anusuya, V. Veera Santhiya, R. Vazhan Arul Srimathi, R. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350384369

image Captioning (ICs) seamlessly combines the realms of Computer Vision (CV) and Natural Language processing (NLP) task involved in producing textual sentences that summarise the image content in a way which is understandable to humans. The primary objective is to enable machines to convey the content of images in human-readable language. image understanding and text formation are typically the two primary processes in the image captioning process. A family of Convolutional Neural Network (CNN) is used to retrieve visual information from an input image during the image understanding process. This visual information includes the image's scenes, relationships, and objects. To translate these visual features into coherent and contextually appropriate captions, recurrent neural network, long short-term memory, transformer-based models, etc. are used in the text generation step. In this article, we propose a survey about the previous research paper and the fundamental steps involved in image captioning tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Research Progress of Improved Conventional image Edge Detection Algorithm 2nd

Research Progress of Improved Conventional Image Edge Detect...

引用

2nd International conference on image, Vision and Intelligent systems, ICIVIS 2022

作者： Zhang, Meng Jia, Lina School of Information Science and Engineering Zaozhuang University Zaozhuang China Institute of EduInfo Science and Engineering Nanjing Normal University Nanjing China

ISBN: (纸本)9789819909223

image edges contain most of the information in an image, which is one of the most basic and important features in an image. image edge extraction occupies a special place in computer vision and image processing, which is the basis for implementing boundary-based image segmentation and target recognition. In order to make more researchers understand the research status of image edge detection, this paper introduces the implementation principles and methods of several traditional image edge detection algorithms, summarizes the new edge detection methods that appear on this basis, analyzes the characteristics and processing performance of various algorithms, and also compares the excellence among different algorithms to provide reference and guidance for researchers to further improve and innovate the algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge detection

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Exploration on Facial image Recognition and processing algorithms under Hadoop

Exploration on Facial Image Recognition and Processing Algor...

引用

2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Gu, Meifang Tang, Hailin Li, Lili Guangdong Baiyun University Institutional Research Center Guangdong Guangzhou China Guangdong Baiyun University Faculty of Intelligent Manufacturing Engineering Guangdong Guangzhou China Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China

ISBN: (纸本)9798350315455

Face recognition is an important technology in the field of digital image processing, which has a crucial impact on subsequent work. This article introduced an example of facial recognition based on the combination of Hadoop algorithm and facial feature extraction. The experimental results were validated in the MATLAB environment and trained using neural networks to improve system performance and adaptability. The test results indicated that the processing speed was between 0.84 and 0.97;the highest resource utilization rate was 0.06, and the lowest was 0.02;the throughput range of facial data was between 13598 and 15479, and the throughput range of user data was between 5037 and 5879. This indicated that the system had fast recognition speed and high-precision image output, and exhibited good fault tolerance and robustness compared to other methods. Exploring facial image recognition and processing algorithms in the Hadoop environment can utilize Hadoop's distributed computing capabilities to accelerate the processing of large-scale facial image data, achieve fast and efficient processing of large-scale facial image data, accelerate algorithm operation, and possess distributed computing capabilities. Multiple computing nodes can be used for parallel processing to improve algorithm performance. © 2023 IEEE.

关键词： algorithm research facial recognition Hadoop algorithm image processing

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Advancements in Hyperspectral image processing Using Machine Learning

Advancements in Hyperspectral Image Processing Using Machine...

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2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication systems, ICITEICS 2024

作者： Ramani, Kasarapu Yukitha, P. Karthik, Sake Chowdary, Karapakula Aravind Akash, Mandapalli Dept of Data Science Tirupati India Sree Vidyanikethan Engineering College Department of Csse Tirupati India

ISBN: (纸本)9798350382693

In several domains, such as remote sensing, agriculture, and environmental monitoring, hyperspectral image processing is essential. In this work, the Indian Pines dataset is used to investigate hyperspectral picture classification. Five machine learning algorithms - Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost) are assessed for performance. It improves classification accuracy for preprocessing methods such as feature extraction, PCA and noise reduction. The effectiveness of each method is evaluated using performance indicators such as kappa coefficient, average accuracy, and overall accuracy. Our results highlight how well XG Boost performs in obtaining the maximum accuracy of 96.12% when compared to other models. © 2024 IEEE.

关键词： Convolutional neural networks

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Dual-band image Sequence Registration based on Description of Velocity and Curvature Distribution 16

Dual-band Image Sequence Registration based on Description o...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Zhao, Bingqing Tang, Shanjun Li, Chuchen Yang, Beier Yang, Qingyu Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

The composite seeker based on multi-band imaging detection is the development focus of future precision-guided weapons. A certain seeker adopts a recognition and tracking strategy based on the fusion of long-wave infrared and visible light images. The quality of the fused image is closely related to the image registration accuracy. In order to solve the problem that the feature description of infrared and visible dual-band images is not identical due to large difference in grayscale texture information, we propose a registration algorithm based on description of velocity and curvature distribution. In particular, we first extract the infrared and visible light target foreground, and use the created velocity distribution description to complete the FAST corner point matching on the foreground contour. All the matched point pairs are used to achieve coarse contour registration. Then, we use the created curvature distribution description to complete the CSS corner point matching of the target foreground. Finally, a real-time updated matching reservoir is built to calculate the fine registration matrix. The experimental results of multiple sets of actual infrared/visible light image sequences show that the proposed algorithm can achieve better feature description identity and better registration accuracy than the other algorithms. © 2025 SPIE.

关键词： image fusion

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Alzheimer's Disease Classification and Severity Level Identification in image processing using Deep Learning algorithms 2

Alzheimer's Disease Classification and Severity Level Identi...

引用

2nd International conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Devi, T. Sangeetha Raghavendran, V Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci Sch Comp Sci Chennai 600117 Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Alzheimer's disease (AD) is a neurodegenerative condition that deteriorates brain cells and impairs a patient's memory. It is progressive and incurable. Early identification can shield the patient from more brain cell damage and, as a result, help them avoid irreversible memory loss. The scientific community has employed a number of deep learning algorithms to automatically identify Alzheimer's patients. These comprise binary classification of patient scans into stages of AD as well as moderate cognitive impairment (MCI). Limited research has been done on the multiclass classification of Alzheimer's disease (AD) up to six distinct stages. This research proposes novel technique in Alzheimer disease detection with severity level analysis utilizing deep learning (DL) model. Input is collected as MRI brain images and processed for noise removal and smoothening. Then processed image classification and disease stage is detected using pre-trained multi-layer convolutional residual transfer Random Forest with InceptionV3 model. Experimental analysis is carried out in terms of training accuracy, mean average mean average precision, sensitivity, AUC for various MRI brain image dataset. Training accuracy attained by proposed technique is 96%, mean average precision of 93%, sensitivity of 95%, AUC of 90%.

关键词： Alzheimer disease detection severity level analysis deep learning model InceptionV3 Random Forest

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PiVisionSort: Integrating image processing and Machine Learning for Material Recognition on Conveyor Belts 2

PiVisionSort: Integrating Image Processing and Machine Learn...

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2nd International conference on Data, Information and Computing Science, CDICS 2024

作者： Kia, Shohreh Leiding, Benjamin Technische Universität Clausthal Institute for Software and System Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9781643685762

This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art image processing and machine learning techniques to automate the identification of various materials, thereby enhancing operational efficiency and accuracy in industrial settings. Current methods in material detection, such as traditional manual sorting and basic automated systems, often need more precision and adaptability in dynamic industrial environments. This paper identifies a gap in the practical and reliable detection of diverse materials under varying environmental conditions. The primary research questions addressed include: How can modern image processing and machine learning techniques improve material detection accuracy? What optimizations can be applied to ensure real-time processing efficiency? The proposed solution integrates a strategic camera setup with controlled lighting, robust image preprocessing algorithms (noise reduction, normalization, and resizing), and custom-designed detection algorithms using machine learning models. This system outperforms existing solutions by offering higher detection accuracy and adaptability to diverse industrial conditions. An interface is developed to display detection results and provide intuitive controls for system adjustments, ensuring practical use by industrial professionals. Rigorous testing and validation processes are implemented to enhance detection accuracy and processing speeds, with specific performance metrics established to measure efficacy. This paper provides a transformative impact on the manufacturing and processing industries by addressing environmental variability and material diversity. © 2025 The Authors.

关键词： Noise abatement

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