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

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Military vehicles marking detection algorithm on a digital image 4

Military vehicles marking detection algorithm on a digital i...

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Skyruta, Vladyslav Yurchuk, Iryna Software Systems and Technologies department Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (纸本)9798350395532

Constant complicating of wars due to considerable development of military weapon and moving to new generation of warfare led to a significant change in means of it. More and more armed forces involve AI into solving a wide range of tasks, since modern war produces a lot of data which need to be processed and analyzed. In this work, an effective military vehicles marking detection algorithm was developed in order to make it possible to determine the ownership of military equipment based on its marking. It uses pure image processing algorithms instead of involving neural networks, like in R-CNN model or its derivatives. As a result, the algorithm does not require a huge dataset of marking templates and time for network training and can be applied immediately. The algorithm is not bound to a specific marking pattern and can be applied to any. Also it is designed for detecting military marking without reference to objects. As a result, the algorithm can be easily integrated with other image processing systems, e.g. military vehicles detection. © 2023 IEEE.

关键词： Template matching

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Deep learning in the design and application of thoracic medical image-Assisted diagnosis algorithm 7

Deep learning in the design and application of thoracic medi...

引用

7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xiao, Yineng The Global Intellectual Property Institute Nanjing University Suzhou215163 China Advanced Institute of Information Technology Peking University Hangzhou311200 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

In recent years, with the development of deep learning (DL) technology and the continuous improvement of algorithms, DL-Assisted diagnosis systems based on medical images have rapidly developed. Compared with traditional image processing methods, DL trains models by combining a large amount of relevant data (e.g. clinical data, imaging data, etc.) and then uses the models to predict disease-related information. Compared with traditional medical image processing algorithms, DL has better performance in recognition, segmentation and classification of medical images. In this paper, an AI-Aided diagnosis algorithm is developed for lung cancer, a malignant tumour disease, based on clinical chest CT data and imaging data. The algorithm uses chest CT images as the object of study, and classifies and evaluates patients based on various factors such as their age, gender, tumour volume and location, as well as their knowledge of the disease. A pre-Trained model was first used to establish an algorithm for lung cancer tumour segmentation and recognition. Convolutional NNs are then applied to learn solutions to the feature extraction and classification problems. Finally, the results obtained are used as model output and the system performance is evaluated to complete the diagnostic process for lung cancer as a class of malignant tumour disease. © 2023 SPIE. All rights reserved.

关键词： Diseases

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Research on Ink Style image Generation Based on Deep Learning

Research on Ink Style Image Generation Based on Deep Learnin...

引用

2023 International conference on Electronic Information Engineering and Data processing, EIEDP 2023

作者： Zheng, Tianyi Huang, Zhangqin Zhang, Runmin Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China

ISBN: (纸本)9781510666160

Most of the current existing style transfer methods are based on photographs or Western paintings. Due to the inherent differences between Chinese and Western paintings, direct application of existing algorithms cannot generate satisfactory style transfer results for Chinese ink paintings. Accordingly, I propose a new feedforward style transfer method [1], called inkStyle, which uses a draw Network to transfer global style patterns at low resolution and a higher resolution details Networks to modify local style patterns in a pyramidal manner based on the multi-level Laplacian filtering output of the content image. The experimental results show that the method in the paper performs better and produces better visual results. © 2023 SPIE.

关键词： Deep learning

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ResNet-50 for Flower image Classification: A Comparative Study of Segmentation and Non-Segmentation Approaches 19

ResNet-50 for Flower Image Classification: A Comparative Stu...

引用

19th International conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Manga, Abdul Rachman Nirmala Azis, Huzain Fattah, Farniwati Salim, Yulita Darwis, Herdianti Departement of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

Flower image classification poses a challenge in digital image processing, requiring effective methods for feature extraction and classification. The aim of this research is to improve the accuracy of flower image classification by employing ResNet-50 for feature extraction, with and without segmentation, and evaluating the effectiveness of various classification algorithms. The dataset consists of images of Calendula and Coreopsis flowers, totaling 2,025 samples. Four segmentation techniques—Canny, Thresholding, Otsu, and Mean Shift—along with a non-segmentation approach are applied. The features extracted using ResNet-50 are classified with Naive Bayes, Support Vector Machine (SVM), Decision Tree, and K-Neighbors Classifier (KNN). Performance evaluation is conducted using accuracy, precision, recall, and F1-score with 5-fold cross-validation. The results show that SVM delivers the best performance in most scenarios. The highest accuracy in segmentation scenarios was achieved with the Mean Shift technique at 0.91, while the non-segmentation approach yielded the highest accuracy of 0.95. The non-segmentation approach proves more effective, indicating segmentation is not always required for high classification accuracy with ResNet-50. This study shows that ResNet-50, especially without segmentation, can significantly improve flower image classification compared to traditional methods, opening opportunities for more efficient systems. © 2025 IEEE.

关键词： Support vector machines

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Fairness-aware Dimensionality Reduction 31

Fairness-aware Dimensionality Reduction

引用

31st European Signal processing conference, EUSIPCO 2023

作者： Kose, O. Deniz Shen, Yanning Electrical Engineering and Computer Science University of California Irvine Irvine United States

ISBN: (纸本)9789464593600

In this era of digitalization, the massive increase in available data leads to great potential for advancing various domains. However, the available data, such as images, videos, and speech signals, generally lie in high-dimensional space, which calls for efficient dimensionality reduction techniques to process them efficiently. Furthermore, while the fairness of algorithms is essential for their deployment in real-world systems, the effect of dimensionality reduction on fairness is an under-explored research area. Motivated by this, this paper puts forth a fairness-aware dimensionality reduction framework that is capable of properly compressing the data while mitigating bias. Specifically, our design targets at reducing the correlation between the compressed data and sensitive attributes, while projecting the data into a new coordinate system where most of its variation can be described. Experimental results on the CelebA dataset demonstrate that the proposed dimensionality reduction framework can improve group fairness measures for image classification while providing comparable utility to the conventional techniques. © 2023 European Signal processing conference, EUSIPCO. All rights reserved.

关键词： image classification

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Intelligentization of film and television post-production based on complex image processing and scene switching algorithms 4

Intelligentization of film and television post-production ba...

引用

4th International conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Wang, Jiangtao Wang, Qi College of Art Hebei University Baoding071000 China

ISBN: (纸本)9781665401180

Intelligentization of the film and television post-production based on the complex image processing and scene switching algorithms is studied in the paper. At present, the film and television media is in a period of rapid de velopment, and new media formats are constantly emerging. The emergence of high-definition television HDTV, the rapid development of the network puts forward new requirements for film and television post-production, in order to adapt to these changes, various software manufacturers continue to improve software functions and keep pace with the *** development of computer virtual technology and the application of the design field have caused a fundamental change in the traditional design process. Therefore, the handover procedure caused by any delay should be minimized, which has attracted the attention of many researchers and seeking new solutions. The novel solutions are proposed model improve the complex image processing and scene switching algorithm performance. Through the verification, the performance is guaranteed. © 2022 IEEE

关键词： Digital television

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International conference on Software, systems and Information Technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： image segmentation

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Role, Significance, and Impact of Method Noise in Multi-Modal Medical image Fusion 5

Role, Significance, and Impact of Method Noise in Multi-Moda...

引用

5th International conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Bhosekar, Shailesh Singh, Prabhishek Garg, Deepak Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798331522100

Multi modal medical image fusion in, for instance, combining information from computed tomography (CT) scans, positron emission tomography (PET) and magnetic resonance imaging (MRI) into one single dataset, improves the accuracy of diagnostics due to atomistic and functional details of the images. In this paper, the role, importance, and effect of method noise in multi-modal medical image fusion, is considered to assess and improve fusion algorithms. The effectiveness of an algorithm can be determined in terms of its efficacy at preserving salient image content and removing unwanted artifacts while maintaining the former, which is defined by method noise, the residual noise after image processing. In this paper, I review the theoretical basis of method noise, discuss its function in both fusion and denoising processes, and discuss its practical and conceptual implications, showing its importance for quality assessment and algorithm refinement. We apply method noise to the Non-Subsampled Shearlet Transform (NSST) to demonstrate application in multi-modal medical image fusion. Comparative experiments using NSST based fusion with /without method noise demonstrate significantly different visual clarity and quantitative metrics as measured with different metrics. Results show that method noise both maintains image integrity by preventing detail loss and aids in the integration of complementary detail from different modalities. The need for method noise in advancing image fusion techniques is emphasized in this paper, and a framework for future research and application of fusion in diagnostic imaging is presented. © 2025 IEEE.

关键词： Positron emission tomography

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AI-Enhanced image Translation for Seamless Communication in Global Tourism

AI-Enhanced Image Translation for Seamless Communication in ...

引用

2024 International conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Rubasri, S. Shafiur Rahman, N.F. Syed Khalid, J. Vignesh Balaji, R. Prasath, R. Krishna, B.V. Kcg College Of Technology Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

An inventive program called Jargon Lens was created to meet the various language requirements of international visitors. AI-enhanced image translation technology, jargon lens empowers travelers to effortlessly overcome language barriers by seamlessly translating text in images to their preferred language. The core of this project is to create a user-friendly mobile application that can recognize and translate text within images accurately and in real-time. This will enable users to easily understand and communicate in different languages by simply using the Smartphone camera, users can scan or upload and translate text in real-time, ensuring a swift and accurate understanding of their surroundings. Beyond personal advantages, Jargon Lens supports constructive encounters that promote understanding and cultural interchange between visitors and local populations. As a technological solution, Jargon Lens aligns with the evolving landscape of global tourism, where diversity and inclusivity are paramount. This technology combine's computer vision such as optical character recognition (OCR). OCR algorithms analyze the image to recognize and extract the textual content and natural language processing (NLP) to perform the translation. In conclusion, Jargon Lens emerges as a transformative tool for tourists, offering a sophisticated yet easy-to-use solution for navigating language barriers. With its focus on real-time multilingual image translation, Jargon Lens stands as a key enabler for enriched and hassle-free travel experiences, bridging communication gaps in a rapidly evolving global tourism ecosystem. © 2024 IEEE.

关键词： Optical character recognition

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An Efficient image to Speech Generation System for Visually Impaired Individuals Using Deep Learning Techniques 10

An Efficient Image to Speech Generation System for Visually ...

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

作者： Priyanka, G. Nivaashini, M. Shree Akshaya, B. Subashini, M. Ritish, G. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired people. This method leverages deep learning techniques to convert images into meaningful audio outputs. It involves capturing input images and processing them using advanced deep-learning algorithms. The extracted features are translated into speech. The resulting auditory output provides an alternative sensory experience for individuals with visual impairments, enabling them to perceive and interpret visual information through sound. This study implements four deep learning models namely CNN-LSTM, Resnet34, YOLOv5 and Vision Transformer to process sequential visual data capturing intricate patterns and dependencies within images. By integrating these models in this work, the image to audio converter not only achieves superior accuracy in image recognition but also enhances the richness and depth of the auditory output, creating a nuanced and immersive sensory experience for users. The performance of the proposed models was evaluated using several metrics and it was noted that vision transformer outperformed the remaining models. This seamless integration of deep learning models amplifies the study's effectiveness in translating diverse visual inputs into detailed and coherent auditory output, thereby enhancing accessibility and inclusivity for individuals with visual impairments. © 2024 IEEE.

关键词： image enhancement

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