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

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Research on image brightness enhancement algorithm based on deep learning 24

Research on image brightness enhancement algorithm based on ...

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Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Siqi He Jianping Xing Yu Bai Shuzhen Huang Kaiping Sun Peng Sun Shandong University Jinan Shandong Province China Shandong Vocational and Technical University of Engineering Jinan Shandong Province China

ISBN: (纸本)9798400713880

In view of traditional image improvement technology's limitations in high real-time application environments, a new low-light video image improvement algorithm is proposed in this article. In consideration of traditional improvement algorithms' faults, and under the precondition of studying low-light improvement techniques in image signal processing (ISP) systems, deep learning frameworks are introduced to break through complex structure and long processing pipeline faults in traditional ISP systems. In this article, a lightweight image brightness improvement algorithm with a basis in convolutional neural network (CNN) is proposed. In this algorithm, feature extraction of RGB images is performed through CNN to generate a brightness coefficient matrix, simplifying RGB to RGB direct mapping and reducing model numbers, model parameters, and computational complexity. This technology pays attention to generating coefficients of brightness, can effectively extract changing parts of brightness, maintain detail information, and conduct efficient and natural brightness improvement, and can apply to high real-time application environments such as autonomous driving.

关键词： Convolutional Neural Network

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Prediction of Most Confident Labelled Output for Fashion image using Deep Learning Model with Tensorflow

Prediction of Most Confident Labelled Output for Fashion Ima...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International conference on

作者： Saranya M S P. Geetha Dept. of DCSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of science and Technology Chennai India Dept. of DCSE CEG Anna University Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Fashion image classification is an emerging field of research. Examples of numerous e-commerce applications that rely on fashion image classification include recommendation systems, retrieval systems, and apparel recognition tasks. The extensive variety of clothing attributes, including designs, textures, and colours, and the high degree of clothing categorization present formidable challenges to the classification of clothing images. In this paper, a new algorithm called Most Confident Labelled Output (MCLO) algorithm has been proposed. Later, widely used optimization techniques in CNN models like SGD, Adam, AdaGrad and RMSProp are added for classifying the fashion images. Among the CNN optimizers, AdaGrad model achieved better accuracy as well as loss gets decreased. The proposed work was carried on benchmarking Fashion MNIST dataset The MCLO algorithm has been coded using tensorflow with graphics processing unit (GPU). The outcomes demonstrate that the proposed method outperforms state-of-the-art models, as assessed through the Fashion MNIST dataset benchmarks.

关键词： Computational modeling Clothing Graphics processing units Predictive models Benchmark testing Prediction algorithms Classification algorithms

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

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

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Intelligent systems for Cybersecurity (ISCS), International conference on

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

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

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.

关键词： image quality Computer vision Machine learning algorithms Accuracy image color analysis Digital images Computational modeling

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ALOS-2 disaster mapping processing and its implementation in an operational system 26

ALOS-2 disaster mapping processing and its implementation in...

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conference on Sensors, systems, and Next- Generation Satellites XXVI

作者： Ohki, Masato Takakura, Yuki Kawakita, Shiro Koyama, Christian Tadono, Takeo Japan Aerosp Explorat Agcy Earth Observat Res Ctr Ibaraki 3058505 Japan Satellite Applicat & Operat Ctr Chiyoda Ku Surugadai 4-6 Tokyo 1018008 Japan

ISBN: (纸本)9781510655324

Increasing human and economic losses from urban disasters demand synthetic aperture radar (SAR), which has all-weather, day-and-night observation capability. The Japan Aerospace Exploration Agency (JAXA) has been operating the Advanced Land Observing Satellite-2 (ALOS-2), carrying PALSAR-2, an L-band SAR, for monitoring disasters and environmental changes. We developed the first fully automated algorithms for detecting flood extents and earthquake-induced building damage from ALOS-2 data. The algorithms rapidly process ALOS-2 and ancillary data (flood simulation, hazard map, and other geographical information) and provide damage information less than an hour after the input data are entered. The validation results showed that the accuracy of the estimated flood extent was 60 to 94%, depending on the observation conditions, especially the incidence angle of ALOS-2 observation. The accuracy of the damaged building detection was 72% for buildings with a footprint larger than 200 m(2) in the area. We implemented these algorithms in an operational disaster response system. We launched a one-stop operation that automatically processes ALOS-2 data after emergency observation and provides the damage maps rapidly via email for disaster response workers.

关键词： PALSAR-2 space application image processing disaster management disaster monitoring flood building collapse

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A Novel Approach to Memory Disorder Diagnose Using Deep Learning Integration and Adaptive Artificial Approaches

A Novel Approach to Memory Disorder Diagnose Using Deep Lear...

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2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Bhardwaj, Vijay Pandey, Shivam Chandigarh University Department of AIT-CSE Punjab India

ISBN: (纸本)9798350319200

Alzheimer's illness is an untreatable brain condition that gradually erodes cognitive function, early identification can greatly lessen effects. In light of the lack of skilled doctors, the computerized identification of the illness becomes increasingly crucial since it lessens the workload on doctors and improves the accuracy in making the diagnostic. Using segmentation MRI scans, the condition must be precisely diagnosed by a thorough examination of particular brain problem tissues. The collection of pictures taken for dementia detected using MRI scans is divided into four categories: mildly informal (896 pictures), moderately informal (64 photos), not informal (3200 pictures), as well as extremely mildly informal (2240 pictures). The information available is seriously skewed. To solve this problem, we applied the adaptive artificial exceeding method. This method was used, and the information collected was matched. In order to test the effectiveness of the designs, a combination of VGG16 as well as Efficient Net was utilised to identify Alzheimer's disease using both unbalanced and equal information. This method was used, and the information collected was normalised. The suggested approach merged numerous algorithms' projections to create a team approach that extracted intricate and subtle trends within the information at hand. Both algorithms' input and output were merged to create a combined approach, which was subsequently transferred to additional levels to create a more reliable prediction. In this project, we suggested a combination of EfficientNet-B2 and VGG-16 to accurately detect the illness at its earliest stages. Using two databases that are accessible to the public, studies were conducted. According to the findings of the study, the suggested technique obtained 97.85% efficiency and 99.64% (Area Under Curve) for multiple classes information. © 2023 IEEE.

关键词： Alzheimer's illness Compute Vision Deep Learning image processing MRI Brain images Neural Networks

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Compact CNN-based road weather condition detection by grayscale image band for ADAS

Compact CNN-based road weather condition detection by graysc...

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conference on Autonomous systems - Sensors, processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure

作者： Abu-Alrub, Nader J. Abu-Shaqra, Albara D. Rawashdeh, Nathir A. Michigan Technol Univ Dept Elect & Comp Engn Houghton MI 49931 USA Deggendorf Inst Technol Fac Appl Nat Sci & Ind Engn Deggendorf Germany Michigan Technol Univ Dept Appl Comp Houghton MI 49931 USA

ISBN: (纸本)9781510651074;9781510651067

Sensors used in autonomous driving are affected variably in adverse weather conditions. The reliability of each sensor changes with different lighting, precipitation type and intensity, and visibility conditions. Common sensing modalities in autonomous driving include cameras and LiDARs, which have a high resolution signal, but degrade in poor weather conditions. Camera image contrast is also sensitive to changes in lighting. On the other hand, RADARs have a long range, but relatively low spatial resolution. Nonetheless, they are resilient to varying lighting and weather fluctuations. A compact weather detection system can be used to dynamically steer other algorithms used in autonomous vehicles (e.g., vehice control, object detection and tracking) toward the most reliable sensors. By adjusting the weights for different sensors, a fusion scheme can be made to change focus to the currently best performing sensor combination. Alternatively, a weather detection system could be used to switch between weather-specific models or ensemble algorithms. The idea of using multi-model algorithms for autonomous driving has been gaining popularity recently;a model trained in sunny conditions will likely underperform in non-ideal weather conditions. This paper presents a compact Convolutional Neural Network (CNN) that detects current driving weather conditions based on a narrow strip of the grayscale forward facing camera image. Standard multi-class classification metrics are used to assess the performance. The weighted average of the f1-score of all classes was 94%. The model was trained and tested on the RADIATE dataset, which contains multimodal sensor data of driving in different weather conditions including sunny, snow, overcast, fog, and rain.

关键词： Autonomous driving CNN Adverse weather Road surface condition camera ADAS

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Vgg-16 and Vgg-19 Architecture Models in Lie Detection Using image processing 6

Vgg-16 and Vgg-19 Architecture Models in Lie Detection Using...

引用

6th International conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2022

作者： Kusumawati, Dewi Ilham, Amil Ahmad Achmad, Andani Nurtanio, Ingrid Hasanuddin of University Department of Electrical Engineering South Sulawesi Gowa Indonesia Hasanuddin of University Department of Informatics South Sulawesi Gowa Indonesia

ISBN: (纸本)9798350399615

The polygraph, LVA, and VSA are three techniques used to find lying. However, the polygraph monitors the physiological reactions that have been linked to lying rather than detecting lies. Validity and reliability testing for VSA and LVA has been conducted, but the results are not reliable enough and unsatisfactory. How to find a lie detection model using the CNN approach is the goal of this research. With the addition of an augmentation process to image processing, dropouts, and three optimization algorithms-Adam, RMSprop, and Adadelta on the two CNN architecture models with VGG-16 and VGG-19 we use. For the dataset this study used a video-based public dataset, the video was converted into an image format, and a confusion matrix was used to analyze the data during validation. The study found that the accuracy achieved from the VGG-16 model that is, Adam + VGG-16 has the best models were obtained from 18 combinations combining the VGG-16 and VGG-19 models along with other optimizations. © 2022 IEEE.

关键词： Adadelta Adam Classification CNN Detection RMSProp VGG-16 VGG-19

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Performance Comparison of Machine Learning algorithms for Online Handwriting Recognition: A Brief Analysis

Performance Comparison of Machine Learning Algorithms for On...

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Sustainable Expert systems (ICSES), International conference on

作者： Mamta Gurpreet Singh Punit Soni Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

An online handwriting recognition system is a software application that enables the conversion of handwritten text or symbols into digital form. This system utilizes various technologies such as the processing of images, recognition of patterns, the context of machine learning, and computational intelligence to interpret handwritten content accurately. In this system, users input their handwriting through a stylus or mouse, and the software recognizes information and converts it into machine-readable text. The system can be used for a wide range of applications, including the processing of documents, note-taking, and signature verification that impacts the online handwriting system. The accuracy and efficiency of online handwriting recognition systems have improved due to technological breakthroughs in research development, making them a popular choice for various industrial innovation.

关键词： Handwriting recognition Technological innovation Accuracy Machine learning algorithms image recognition Text recognition Symbols Machine learning Software Expert systems

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Research in image processing for Pipeline Crack Detection Applications

Research in Image Processing for Pipeline Crack Detection Ap...

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2022 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Altabey, Wael A. Kouritem, Sallam A. Abouheaf, Mohammed I. Nahas, Nabil Department of Mechanical Engineering Faculty of Engineering Alexandria University Alexandria21544 Egypt Mechatronics Engineering Technology Bowling Green State University Bowling GreenOH43402 United States Université de Moncton Faculté d'Administration MonctonNBE1A 3E9 Canada

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Pipelines, such as gas and utility pipeline systems and networks, are some of the most critical components of civil infrastructure. During the long-term operation of the pipeline, various types of diseases will occur. Pipeline damage may present serious environmental and economical problems. In order to solve the problem that traditional pipelines crack detection, a pipelines crack detection method based on image processing under complex background is proposed. A pipeline crack image segmentation model based on semantic segmentation is built, and cracks in high-resolution crack images are extracted by using the pipeline image segmentation model. The accuracy rate (P%), recall rate (R%), and F-score (F%) of the proposed method are recorded 89.3%,85.7%, and 80.4%, respectively. The results show that, compared with the existing algorithms, the proposed algorithm has a better detection effect and stronger generalization ability in complex pipeline scenes. © 2022 IEEE.

关键词： Semantic Segmentation

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Application of color image encryption algorithm based on logistic sine cosine mapping in image processing

Application of color image encryption algorithm based on log...

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2022 International conference on algorithms, Microchips and Network Applications

作者： Zhang, Jian School of Information Engineering Liaoning Institute of Science and Engineering Jinzhou China

ISBN: (纸本)9781510653290

The rapid development of science and technology has brought convenience for photographing and sharing images. However, with the rapid increase of image data, security problems such as leakage and tampering also occur frequently. The application of image encryption technology is imminent, especially the encryption of color images is in urgent need of improvement and development. The traditional encryption technology is mainly aimed at data stream encryption, which has low efficiency, large amount of calculation and certain limitations. Based on transform domain encryption, the image is transformed from spatial domain to frequency domain for encryption, and then transformed to spatial domain, which belongs to a kind of lossy encryption. Encryption based on chaos has large key space, simple implementation and fast encryption speed. Generally, multiple chaotic systems need to be used to enhance the security of encryption. Therefore, a simple and secure spatial encryption algorithm for RGB three channels of color image is designed in this paper. Firstly, a safer chaotic sequence is generated by logistic sine cosine mapping, and then four rounds of scrambling and diffusion pixels are designed by chaotic sequence. Finally, In a series of security analysis experiments, the security and effectiveness of color image encryption algorithm based on logistic sine cosine mapping are verified. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Mapping

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