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A Comparative Study on Deep CNN Visual Encoders for image Captioning 1

8th International conference on Computer Vision and image processing (CVIP)

作者： Arun, M. Arivazhagan, S. Harinisri, R. Raghavi, P. S. Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi Tamil Nadu India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

Captioning an image is the process of describing it with syntactically and semantically meaningful terms. An image caption generator is developed by the integration of computer vision and natural language processing technology. Despite the fact that numerous techniques for generating image captions have been developed, the result is inadequate and the need for research in this area is still a demanding topic. The human process of describing any image is by seeing, focusing and captioning, which is equivalent that of feature representation, visual encoding and language generation for the image captioning systems. This study presents the construction of a simple deep learning-based image captioning model and investigates the efficacy of different visual encoding methods employed in the model. We have analyzed and compared the performance of six different pre-trained CNN visual encoding models using Bilingual Evaluation Understudy (BLEU) scores.

关键词： image Captioning Flickr8K Visual Encoding BLEU Flickr30K

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Convolutional Neural Network Model to Improve image processing on A C-arm Imaging

Convolutional Neural Network Model to Improve Image Processi...

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2024 International conference on Remote Sensing, Mapping, and image processing, RSMIP 2024

作者： Huang, Jindai Zhang, Jingli Zhang, Dianchen People's Liberation Army 252 Hospital Baoding China

ISBN: (纸本)9781510680012

In the field of medical imaging, C-arm systems play a pivotal role in surgeries, especially interventional surgeries. However, the current C-arm imaging system cannot adapt to the application scenarios due to algorithm limitations. Therefore, in order to improve the quality of diagnostic and therapeutic interventions, it is necessary to improve the quality of C-arm imaging and to fulfill the various requirements of different scenarios by improving the image processing techniques associated with C-arm imaging. This article explores how to improve C-arm image processing using convolutional neural network (CNN)-related algorithms. CNNs are known for their ability to extract complex features from images, which can be used for contrast enhancement, noise reduction, artifact removal, and resolution enhancement. Through a comprehensive review of state-of-the-art applications and technologies, this study proposes an innovative method of applying CNN for image processing in C-arm systems, improving image quality and thus providing potential advancements in clinical diagnosis and surgery. In terms of real-time image enhancement, integrating CNN into the C-arm imaging pipeline has demonstrated promising results, providing a reference for medical imaging learning and research. This article discusses the methods, experimental results, and implications of incorporating CNN into C-arm imaging, emphasizing that this technology can greatly improve image quality and indicating the potential of artificial intelligence technology in C-arm imaging applications. © 2024 SPIE.

关键词： Medical imaging

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Real-Time Dehazing and Defogging: A Comprehensive Analysis for Single image Haze and Fog Removal

Real-Time Dehazing and Defogging: A Comprehensive Analysis f...

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

作者： Kumari, Apurva Kumar, A. Pavan Teja, B.V.S. Tarun B v Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India

ISBN: (纸本)9798350382693

Implementing image dehazing and defogging on a Field Programmable Gate Array (FPGA) offers efficiency. Dehazing an image becomes particularly challenging in the presence of fog or haze. However, employing a dark channel prior to dehazing allows the removal of haze particles from the image. Different modules are used in this process, such as Dynamic Atmospheric Light Estimation (DALE), Scene Recovery (SR), and Transmission Map Estimation (TME). The FPGA runs these modules in hardware and produces effective outcomes by employing image processing algorithms. In this case, using FPGA technology offers a number of benefits. The dehazing process can be accelerated by using FPGA's built-in parallel processing capabilities to execute numerous operations at once. Furthermore, FPGA implementations provide better throughput and reduced latency in comparison to conventional approaches, making them well-suited for real-time applications such as image dehazing and D © 2024 IEEE.

关键词： image analysis

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Comparison of Learned image Compression Methods and JPEG

Comparison of Learned Image Compression Methods and JPEG

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2024 Innovations in Intelligent systems and Applications conference, ASYU 2024

作者： Öztürk, Emir Mesut, Altan Computer Engineering Department Faculty of Engineering Trakya University Edirne Turkey

ISBN: (纸本)9798350379433

Media such as audio, images, and videos, which occupy significant storage space in digital environments, are often compressed to save space, particularly outside professional settings. The compression process aims to achieve space savings while maintaining an acceptable level of quality. Many standard image compression algorithms are based on the principle of eliminating data that the human eye cannot perceive and selectively removing certain data after performing spatial transformation on the obtained image. In recent years, compression algorithms utilizing autoencoders or generative adversarial network (GAN) architectures have emerged. These algorithms fundamentally aim to reduce the dimensionality of data and create a representation of it. The dimension reduction stage used is considered equivalent to the compression process. In this study, the performance measurement of three different models using autoencoders and GANs is conducted, and the results are compared with the performance of the JPEG algorithm in terms of speeds and ratio. Upon examining the results, it is observed that learned image compression methods are catching up with JPEG in terms of quality. The methods achieve better results in terms of compression ratio compared to JPEG, but they operate much slower than JPEG in terms of processing time and have some artificial artifacts. © 2024 IEEE.

关键词： Generative adversarial networks

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Wind Turbine Gearbox Anomaly Detection Using Signal-to-image processing algorithms and Convolutional Autoencoder 21

Wind Turbine Gearbox Anomaly Detection Using Signal-to-Image...

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21st European Energy Research Alliance Meeting

作者： Sheikhi, S. Keprate, A. Ghose, D. Kristiania Univ Coll Dept Econ Innovat & Technol Oslo Norway Oslo Metropolitan Univ Dept Mech Elect & Chem Engn GronnMet Green Energy Lab Oslo Norway Kristiania Univ Coll Sch Econ Innovat & Technol Bergen Norway

In this study, signal-to-image conversion techniques coupled with a convolutional auto encoder (CAE) are used for the detection of anomalies in the wind turbine (WT) gearbox system. Firstly, the time series data is converted to images using six different algorithms. Thereafter, these images are stacked into the multi-dimensional structures known as "data cubes", which is finally fed into CAE for the anomaly identification. The results of this study demonstrate the enhanced efficacy of the method specially in the Gramian Angular Field model in detecting anomalies accurately, suggesting a viable path towards the implementation of dependable and affordable WT monitoring systems. This will open the door for the renewable energy industry's condition monitoring procedures to become more automated and digitalized.

关键词： Wind turbines

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Real Time image processing for Autonomous Vehicles

Real Time Image Processing for Autonomous Vehicles

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2024 Asian conference on Intelligent Technologies, ACOIT 2024

作者： Chandrashekhar, Mhamane Sanjeev Bachute, Bhagyashri Amol Gadgoli, Amruta K. Rankhamb, Dinesh Dattatraya Madri, Shrinivas Department of Electronics and Telecommunication Engineering Shree Siddheshwar Women's College of Engineering Maharashtra Solapur413002 India Department of Artificial Intelligence & Data Science VVP Institute of Technology Maharashtra Solapur413008 India

ISBN: (纸本)9798350374933

Autonomous vehicles require real-time image processing to improve their capabilities by allowing them to understand and respond appropriately to their environment. This paper examines the present state of real-time image processing for self-driving vehicles, including the techniques employed, challenges, and advancements. This article investigates methods such as semantic segmentation, object recognition, categorization, and depth estimation, with an emphasis on enhancing vehicle perception, navigation, and decision-making. The article delves into the implementations of significant algorithms on embedded platforms, their computational efficiency, and their deployment in real-world scenarios. In conclusion, the report investigates prospective avenues for additional research to improve the reliability and efficiency of the real-time image processing systems of autonomous automobiles. © 2024 IEEE.

关键词： Semantic Segmentation

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Semantic Web-Based AI System for Neuroimmune-Gastrointestinal Medical image processing

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION systems 2025年第1期21卷

作者： Guan, Xueyu Guo, Lizhong Qin, Xuewen Santos, Javier Gonzalez, Ana Maria Qin, Yi Chifeng Univ Chifeng Canc Hosp Affiliated Hosp 2 Canc Res Ctr Chifeng CityMed Dept 2 Chifeng 024000 Peoples R China Peking Univ Clin Med Coll 9 Dept Med Beijing Peoples R China Vall dHebron Hosp Univ Vall dHebron Inst Recerca VHIR Digest Syst Res Unit Lab Neuroimmuno Gastroenterol Barcelona Spain Vall dHebron Hosp Univ Dept Gastroenterol Barcelona Spain Inst Salud Carlos III Ctr Invest Biomed Red Enfermedades Hepaticasy Dige Madrid Spain Univ Autonoma Barcelona Fac Med Bellaterra 08035 Spain

At present, the medical field requires the development of clinical diagnostic and therapeutic robots. The research team successfully transformed the biophysical structures of mitochondria and lipid particles from 2D to 3D. Based on cloud point registration algorithm and SVM, LSSVM, and Bayesian algorithms, the predicted cloud point was added to the cloud point volume to increase the cloud point data capacity and combined with psychological analysis of 3D cloud point structure hierarchical clustering. The K-SVD algorithm is used to deconvolve pathological data to reduce color interference on visual transformers, fit 3D data of midbrain pathways, and finally fuse 2D data converted from pathological data, mandala data, and immune factor data with brain computer interface data for classification prediction, improving the robustness of artificial intelligence diagnostic algorithms. This information fusion robot can accurately diagnose the comprehensive condition of patients and contribute to medical assisted diagnosis.

关键词： Clinical System Artificial Intelligence Information Fusion 3D images Biomedical Engineering

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Design Of An Improved Dish Recognition Algorithm Based On image Retrieval 2

Design Of An Improved Dish Recognition Algorithm Based On Im...

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2nd International conference on Machine Vision, image processing and Imaging Technology, MVIPIT 2024

作者： Yu, Yi Liu, Jie Sebial, Archival Canillo, Angie Ceniza Jiangxi Vocational College of Industry and Engineering China Department of Computer Information Sciences and Mathematics School of Arts and Sciences University of San Carlos Cebu Philippines

ISBN: (纸本)9798331543037

With the booming of the new retail industry, unmanned billing systems have become the key to improve the operational efficiency of restaurants and reduce labour costs. In the field of payment and billing, traditional recognition systems based on deep neural networks often rely on a single network to achieve the detection and classification of Chinese dishes, but in the face of the increase or decrease in the number of dishes, it is necessary to frequently adjust the type of the network output and re-collect a large amount of image data for training, which leads to high update costs. In this study, a deep learning EfficientNet convolutional neural network combined with image retrieval for dish recognition is used to solve this problem. Firstly, EfficientNet, an advanced dish detection network, is used to accurately locate the positions of multiple dishes in an image, determine their orientations, and effectively separate the dishes from the background. Subsequently, the EfficientNet based feature extraction network calculates the image features of the separated dishes and uses cosine similarity as a metric to efficiently retrieve the dishes in the reference dish library, thus obtaining the classification results of the dishes. Finally, the system outputs the precise location and category information of the dishes. Experimental results show that the EfficientNet and cosine similarity approach achieves an average accuracy of 98.3% on the dish image dataset of China Food Network, which not only significantly shortens the updating time compared with traditional algorithms, but also requires only 1/24 of the amount of image data of the traditional deep learning classification *** breakthrough effectively solves the problem of the difficulty and high cost of updating the current dish recognition algorithms. This breakthrough effectively solves the current problem of difficult and costly updating of dish recognition algorithms, and is expected to significantly redu

关键词： Convolutional neural networks

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image processing-Based Dust Detection for Solar Panels

Image Processing-Based Dust Detection for Solar Panels

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2024 International conference on Smart systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Sree Sowndarya Barani, K. Keerthana, S. Hariharasudhan, S. Sundaramn, Mohana Kumaraguru College of Technology B.E. Electrical and Electronics Engineering Tamilnadu India Kumaraguru College of Technology Electrical and Electronics Engineering Tamilnadu India

ISBN: (纸本)9798350378177

This study focuses on dust detection in solar panels by utilizing image processing techniques. Dust detection helps in forecasting the maintenance needs and ensures system reliability. Inefficient maintenance practices of solar panels lead to increased downtime and decreased energy production. Implementation of dust detection holds importance in ensuring consistent power output. In the existing methodologies, Artificial Neural Network algorithms are implemented for dust detection. Artificial Neural Network algorithms show a considerably lower accuracy in image processing due to overfitting and complexity of image data. In the proposed system, a comparative analysis is performed by implementing Multi-Layer Perceptron technique and Dense Net algorithm. Using Multi-Layer Perceptron, a Sequential model is built with one flatten layer and two dense layers. It shows an accuracy of 88%. The Dense Net algorithm was also implemented on the processed image dataset and it shows an accuracy of 98%. This helps in increasing the accuracy and minimizing error. The Thonny IDE is employed for the implementation of the models, with validation conducted using hardware components such as Raspberry Pi, solar panels, and camera modules. Real-time images are captured to assess the performance. The utilization of the Convolution Neural Network algorithm, specifically Dense Net, for dust detection has led to enhanced system accuracy and efficiency. © 2024 IEEE.

关键词： Solar concentrators

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Method for background replacement of certification photos

Method for background replacement of certification photos

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2024 International conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Chen, Xu Zhang, Jihong Du, Yufei Sha, Zihuan Qiu, Rongfei Xu, Zezhong School of Computer Science and Information Engineering Changzhou Institute of Technology Jiangsu Changzhou213032 China

ISBN: (纸本)9781510685437

In order to overcome defects resulted from replacing the photo background color, an improved method is proposed for background replacement. The α-values in the alpha matte are transformed to enhance the details in the transition area. The new photo is composited by comprehensively considering the foreground portrait, new background, old background and the alpha matte. The experimental results show that the foreground portrait appears clearer and more complete, and the transition area between the foreground and background is more harmonious and more natural. © 2024 SPIE.

关键词： Color Opacity Computer science Engineering image processing image processing algorithms and systems image processing software image segmentation Information science

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