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artificial Intelligence and image and Signal processing for Remote Sensing XXX 2024

作者： Akin, Suleyman Emir Akgun, Toygar Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey

ISBN: (纸本)9781510681002

Infrared (IR) imaging sensors designed to acquire the 0.9-14 micrometers wavelength band offer unique advantages over the daylight cameras for a multitude of consumer, industrial and defense applications. However, IR images lack natural color information and can be quite challenging to interpret without sensor specific training. As a result, transforming IR images into perceptually realistic color images is a valuable research problem with a substantial potential for commercial value. Recently, various research works that use deep neural networks to colorize single mode (near or thermal) infrared images have been reported. In this paper, we present a novel convolutional auto-encoder architecture that takes multiple images captured with different imaging modes (near IR, thermal IR and low-light) to perform colorization using the visual cues that exist in all imaging modes. We present visual results demonstrating that using multiple IR imaging modes improves the overall visual quality of the results. © 2024 SPIE.

关键词： Deep neural networks

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Prediction of Pathogen Causing Rice Plant Disease and Recommendation using Enhanced Machine Learning Technique 3

Prediction of Pathogen Causing Rice Plant Disease and Recomm...

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3rd International Conference on Applied artificial Intelligence and Computing, ICAAIC 2024

作者： Priya, J Suji Priya, M Hema Iyswarya, M. Kiruthika, K. Sona College of Technology Department of Master of Computer Applications Salem India

ISBN: (纸本)9798350375190

Plant diseases pose significant challenges to agricultural productivity, impacting both the quality and quantity of crop yields. Early detection and effective management of these diseases are essential for mitigating their detrimental effects on agricultural output. However, traditional methods of disease monitoring and diagnosis are often costly and require continuous expert intervention. To address these challenges, this study proposes a novel approach that combines image processing techniques and advanced machine learning methodologies for the accurate identification and classification of rice leaf diseases. By utilizing datasets comprising images of diseased rice leaves, environmental data, and historical disease reports, the proposed system utilizes Convolutional neural networks (CNNs) for image analysis and feature extraction. Subsequently, a Support Vector Machine (SVM) classifier is employed for disease detection and classification based on the extracted features. The integration of image processing and machine learning technologies offers a cost-effective and efficient solution for early disease detection and management in agriculture. By automating the process of disease identification, the proposed system facilitates timely interventions, thereby minimizing crop losses and promoting agricultural sustainability. © 2024 IEEE.

关键词： Convolutional neural networks

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artificial intelligence in endodontics: Fundamental principles, workflow, and tasks

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INTERNATIONAL ENDODONTIC JOURNAL 2024年第11期57卷 1546-1565页

作者： Ourang, Seyed AmirHossein Sohrabniya, Fatemeh Mohammad-Rahimi, Hossein Dianat, Omid Aminoshariae, Anita Nagendrababu, Venkateshbabu Dummer, Paul Michael Howell Duncan, Henry F. Nosrat, Ali Shahid Beheshti Univ Med Sci Res Inst Dent Sci Dentofacial Deform Res Ctr Tehran Iran ITU WHO Focus Grp Hlth Top Grp Dent Diag & Digital Dent Berlin Germany Univ Maryland Sch Dent Dept Adv Oral Sci & Therapeut Div Endodont 650 West Baltimore St4th floor Baltimore MD 21201 USA Private Practice Irvine Endodont Irvine CA USA Case Western Reserve Univ Sch Dent Med Dept Endodont Cleveland OH USA Univ Sharjah Coll Dent Med Dept Restorat Dent Sharjah U Arab Emirates Cardiff Univ Coll Biomed & Life Sci Sch Dent Cardiff Wales Dublin Dent Univ Hosp Trinity Coll Dublin Div Restorat Dent Dublin Ireland Centreville Endodont Private Practice Centreville VA USA

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures;(B) explain the workflow for developing AI solutions, from data collection through clinical integration;(C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

关键词： artificial intelligence deep learning endodontics machine learning neural networks

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Research on Video Sample Collection and processing Methods Based on artificial Intelligence Platform 11th

Research on Video Sample Collection and Processing Methods B...

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11th International Conference on applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Hu, An Wang, Qi Xu, Xiaoguang Zhao, Yao Ji, Qian Pei, Lei State Grid Henan Information and Telecommunication Company Henan Province Zhengzhou450052 China State Grid Henan Electric power Company Henan Province Zhengzhou450052 China Henan Jiuyu Tenglong Information Engineering Co. Ltd Henan Province Zhengzhou450052 China

This paper summarizes the video sample collection and processing methods based on artificial intelligence platform, focusing on video noise cancellation, content segmentation and classification, and feature extraction and representation techniques. The paper believes that the deep learning technology, especially the convolutional neural network, shows great potential in image recognition and video analysis, and effectively improves the level of automation and accuracy of video processing. This paper discusses the importance of building a large-scale and high-quality video sample library, and how to improve the processing efficiency and accuracy of video data through intelligent technology. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine

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IEEE ACCESS 2025年 13卷 43289-43315页

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Inst Telecomunicacoes P-1049001 Lisbon Portugal Politecn Leiria ESTG P-2411901 Leiria Portugal Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.

关键词： Transform coding Point cloud compression Encoding Standards image coding Three-dimensional displays Geometry image color analysis artificial intelligence Codecs JPEG Pleno standard learning-based coding man and machine point cloud coding

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Convolutional neural networks Towards Facial Skin Lesions Detection 20

Convolutional Neural Networks Towards Facial Skin Lesions De...

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20th CSI International Symposium on artificial Intelligence and Signal processing, AISP 2024

作者： Sarshar, Reza Heydari, Mohammad Noughabi, Elham Akhondzadeh Tarbiat Modares University School of Industrial and Systems Engineering Tehran Iran

ISBN: (纸本)9798350383942

Facial analysis has emerged as a prominent area of research with diverse applications, including cosmetic surgery programs, the beauty industry, photography, and entertainment. Manipulating patient images often necessitates professional image processing software. This study contributes by providing a model that facilitates the detection of blemishes and skin lesions on facial images through a convolutional neural network and machine learning approach. The proposed method offers advantages such as simple architecture, speed and suitability for image processing while avoiding the complexities associated with traditional methods. The model comprises four main steps: area selection, scanning the chosen region, lesion diagnosis, and marking the identified lesion. Raw data for this research were collected from a reputable clinic in Tehran specializing in skincare and beauty services. The dataset includes administrative information, clinical data, and facial and profile images. A total of 2300 patient images were extracted from this raw data. A software tool was developed to crop and label lesions, with input from two treatment experts. In the lesion preparation phase, the selected area was standardized to 50×50 pixels. Subsequently, a convolutional neural network model was employed for lesion labeling. The classification model demonstrated high accuracy, with a measure of 0.98 for healthy skin and 0.97 for lesioned skin specificity. Internal validation involved performance indicators and cross-validation, while external validation compared the model's performance indicators with those of the transfer learning method using the Vgg16 deep network model. Compared to existing studies, the results of this research showcase the efficacy and desirability of the proposed model and methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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Survey of Deep Learning Models for image-Based Disease Detection in Plants 7th

Survey of Deep Learning Models for Image-Based Disease Detec...

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7th International Conference on Soft Computing: Theories and applications, SoCTA 2023

作者： Mathur, Abhishek Shrivastava, Shailendra Ku. Department of Information Technology Samrat Ashok Technological Institute Vidisha India Samrat Ashok Technological Institute Vidisha India

ISBN: (纸本)9789819720880

Crop diseases pose a severe risk to global food supplies, but accurate and timely diagnosis is hampered by a lack of resources in many regions. As the number of people with access to smartphones continues to rise throughout the world, and as recent advancements in computer vision allowed by deep learning pave the way, we may soon be able to use them to aid in the detection of diseases. Acquiring pictures, preprocessing, segmenting, and extracting features are all part of the image processing pipeline for disease detection in plants. In this review study, we take a look back at some of the most popular methods for classifying plant diseases: "Convolutional neural Network (CNN)", "Support Vector Machine (SVM)", "K-Nearest Neighbors", and "artificial neural Network (ANN)". The results of the study show that the convolutional neural network method outperforms the more conventional techniques in terms of accuracy. This review study discusses the use of DL models to depict different plant diseases and gives a detailed description of their operation. Furthermore, several research gaps are noted from which better clarity may be obtained in regard with early detection of plant diseases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： artificial neural network Convolution neural network Deep learning image processing Plant disease detection Recurrent neural network

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HybrUR: A Hybrid Physical-neural Solution for Unsupervised Underwater image Restoration

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IEEE TRANSACTIONS ON image processing 2023年 32卷 5004-5016页

作者： Yan, Shuaizheng Chen, Xingyu Wu, Zhengxing Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat Lab Cognit & Decis Intelligence Complex Syst Beijing 100190 Peoples R China Fuzhou Univ Dept Mech Engn Fuzhou 350000 Peoples R China Xiaobing AI Beijing 100080 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Robust vision restoration of underwater images remains a challenge. Owing to the lack of well-matched underwater and in-air images, unsupervised methods based on the cyclic generative adversarial framework have been widely investigated in recent years. However, when using an end-to-end unsupervised approach with only unpaired image data, mode collapse could occur, and the color correction of the restored images is usually poor. In this paper, we propose a data- and physics-driven unsupervised architecture to perform underwater image restoration from unpaired underwater and in-air images. For effective color correction and quality enhancement, an underwater image degeneration model must be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degeneration theory to design a generator and use neural networks to model the process of underwater visual degeneration. Furthermore, we impose physical constraints on the scene depth and degeneration factors for backscattering estimation to avoid the vanishing gradient problem during the training of the hybrid physical-neural model. Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision. On multiple benchmarks, the proposed method outperforms several state-of-the-art supervised and unsupervised approaches. We demonstrate that our method yields encouraging results in real-world applications.

关键词： Underwater image restoration unsupervised learning style transfer

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A Deep Learning-Based Approach for Part of Speech (PoS) Tagging in the Pashto Language

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IEEE ACCESS 2024年 12卷 86355-86364页

作者： Ullah, Shaheen Ahmad, Riaz Namoun, Abdallah Muhammad, Siraj Ullah, Khalil Hussain, Ibrar Ibrahim, Isa Ali Shaheed Benazir Bhutto Univ SBBU Dept Comp Sci Upper Dir 18050 Khyber Pakhtunk Pakistan Islamic Univ Madinah Fac Comp Sci & Informat Syst Madinah 42351 Saudi Arabia Univ Malakand UOM Dept Software Engn Lower Dir Khyber Pakhtunk Pakistan Univ Malakand UOM Dept Comp Sci & Informat Technol Chakdara 18800 Khyber Pakhtunk Pakistan Fed Univ Technol Owerri Sch Informat & Commun Technol Dept Cybersecur Owerri 460114 Nigeria

A fundamental task in natural language processing (NLP) is part of speech (PoS) tagging. PoS tagging is crucial to many NLP applications, including question answering, machine translation, syntactic parsing, speech recognition, and semantic parsing. PoS tagging is a task for labeling sequences in which a tagger/system tags each word with its appropriate part of speech label. In NLP, PoS tagging is often considered as a language-specific task. Similarly, Pashto is a language that has not been explored regarding PoS tagging. Therefore, this research focuses on the PoS tagging considering the Pashto language and provides a baseline accuracy. The research has twofold benefits. First, it introduces a Pashto tag set that contains 2,81,205 words of the Pashto language. All these words are tagged with 17 unique PoS tags. Second, it proposes a deep learning-based model by examining classic Recursive neural networks (RNN) and Bidirectional Long Short Term Memory networks (BLSTM). The results show promising performances when used with the word embedding technique. The proposed approach achieved 98.82% accuracy as a baseline on the test dataset by using the BLSTM model along with word embedding.

关键词： artificial intelligence document image analysis handwritten text natural language processing optical character recognition speech recognition standard dataset

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Enhanced Feature Extraction for image Dehazing: A Comparative Study between Deep Learning Architectures and FFA-NET 2

Enhanced Feature Extraction for Image Dehazing: A Comparativ...

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2nd International Conference on Inventive Computing and Informatics (ICICI)

作者： Chaudhary, Sarthak Gupta, Samridh Iniyan, S. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai 603203 Tamil Nadu India SRM Inst Sci & Technol Dept Comp Technol Sch Comp Chennai 603203 Tamil Nadu India

ISBN: (纸本)9798350373301;9798350373295

The problem of poor visibility in foggy images has spurred various image de-hazing strategies. As the need for high-quality images grows, especially for autonomous systems, this research aims to leverage different Deep Learning (DL) architectures to draw out key details from images, localizing this retrieved data to mitigate the impact of haze. The work explores using DL methods, particularly contrasting the regression and classification models of Convolutional neural networks (CNN), to remove haze from foggy images. This work sets the stage for further developments in image processing, particularly in conditions with poor visibility. It opens opportunities for improving image quality in various applications, such as autonomous driving and outdoor robotics, where clarity of vision is crucial. The final stage of the proposed model involves three specific pre-processing methods: contextual regularization, air light estimation and boundary constraint for optimal results. The next stage sets out to determine the best DL model for producing clear images from de-hazed ones.

关键词： Machine Learning (ML) Convolutional neural networks (CNN) Feature Fusion Attention Network(FFA-NET) Deep Learning Dark Channel Prior(DCP) artificial Intelligence (AI)

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