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

共 14657 条记录，以下是531-540 订阅

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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...

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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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AI-Enhanced image Translation for Seamless Communication in Global Tourism

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

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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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Keyword-Based Diverse image Retrieval by Semantics-aware Contrastive Learning and Transformer 23

Keyword-Based Diverse Image Retrieval by Semantics-aware Con...

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46th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhao, Minyi Wang, Jinpeng Liao, Dongliang Wang, Yiru Duan, Huanzhong Zhou, Shuigeng Fudan Univ Sch Comp Sci Shanghai Peoples R China Tsinghua Univ Tsinghua Shenzhen Intl Grad Sch Shenzhen Peoples R China Tencent Inc Wechat Grp Guangzhou Peoples R China

ISBN: (纸本)9781450394086

In addition to relevance, diversity is an important yet less studied performance metric of cross-modal image retrieval systems, which is critical to user experience. Existing solutions for diversity-aware image retrieval either explicitly post-process the raw retrieval results from standard retrieval systems or try to learn multi-vector representations of images to represent their diverse semantics. However, neither of them is good enough to balance relevance and diversity. On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing. On the other hand, multi-vector representation methods are not guaranteed to learn robust multiple projections. As a result, irrelevant images and images of rare or unique semantics may be projected inappropriately, which degrades the relevance and diversity of the results generated by some typical algorithms like top-k To cope with these problems, this paper presents a new method called CoLT that tries to generate much more representative and robust representations for accurately classifying images. Specifically, CoLT first extracts semantics-aware image features by enhancing the preliminary representations of an existing one-toone cross-modal system with semantics-aware contrastive learning. Then, a transformer-based token classifier is developed to subsume all the features into their corresponding categories. Finally, a post-processing algorithm is designed to retrieve images from each category to form the final retrieval result. Extensive experiments on two real-world datasets Div400 and Div150Cred show that CoLT can effectively boost diversity, and outperforms the existing methods as a whole (with a higher F1 score).

关键词： Cross-modal retrieval Keyword-based image retrieval Diversification retrieval Transformer

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Design of Multi View Natural Language Query System on Machine Vision 4

Design of Multi View Natural Language Query System on Machin...

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4th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Fang, Quanrong Investment Co. Ltd Beijing China

ISBN: (纸本)9798350359589

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. This article first discusses the system architecture of four modules: data input and preprocessing, visual feature extraction, natural language understanding and matching, and result generation and feedback. Then, the application of machine vision technology in the system was analyzed using two calculation formulas: grayscale conversion and binarization, and natural language processing technology was briefly discussed. Subsequently, a context understanding module was added to construct a multi view natural language query system based on machine vision. Finally, two sets of simulation experiments were conducted to draw the following conclusion: compared with traditional methods, the overall average improvement in image recognition accuracy indicators is about 14.3%, while the overall average improvement in response speed indicators is about 26.5%. This research system can effectively process images from different perspectives and match them with natural language queries. © 2024 IEEE.

关键词： machine vision technology multi view processing natural language query system design

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5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

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5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning. The topics include: Digital Twin Technology Approach Based on the Hierarchical IDP Tensor Decomposition;modeling Technology for Complex Dynamic Operating Environment of Power Grid Based on Digital Twins;design of a Digital Twin Platform Based on Distributed Computing and Resource Optimization algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional Signal processing;time Series Prediction Application of Deep Learning in Multidimensional Signal processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D images Through Integrated Deep Learning and Natural Language processing Techniques;children’s Toy Product Design Based on Augmented Reality Technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, Based on image Features;Visualization and Analysis of CNN Adversarial Training;video Violence Detection Method Based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery Technology Based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text Recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models;a Short Video Retrieval Method Based on Spatio-Temporal Feature Fusion;spatial Data Transformation and Fusion Expression Based on Geometric Algebra;EM2-YOLO: Lightweight Remote Sensing image Detection;Tower-Type Detection of UAV Aerial image Based on YOLOV5 Network Model.

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On image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice 36

On Image Segmentation With Noisy Labels: Characterization an...

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36th conference on Neural Information processing systems (NeurIPS)

作者： Nordstrom, Marcus Hult, Henrik Soderberg, Jonas Lofman, Fredrik KTH Royal Inst Technol Dept Math Stockholm Sweden RaySearch Labs Dept Machine Learning Stockholm Sweden RaySearch Labs Stockholm Sweden

ISBN: (纸本)9781713871088

We study two of the most popular performance metrics in medical image segmentation, Accuracy and Dice, when the target labels are noisy. For both metrics, several statements related to characterization and volume properties of the set of optimal segmentations are proved, and associated experiments are provided. Our main insights are: (i) the volume of the solutions to both metrics may deviate significantly from the expected volume of the target, (ii) the volume of a solution to Accuracy is always less than or equal to the volume of a solution to Dice and (iii) the optimal solutions to both of these metrics coincide when the set of feasible segmentations is constrained to the set of segmentations with the volume equal to the expected volume of the target.

关键词： image segmentation

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An Improved Neural Classification Logic to Predict Kidney Disease Identification Using Medical image processing Principles

An Improved Neural Classification Logic to Predict Kidney Di...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Balaji, C.G. Balachandran, G. Imran, Md. Ujwala, S.Vamsi Edlabadkar, Ajinkya P. Fawareh, Hamed Department of Digital and Telecom Management Lavale Maharashtra Pune412115 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam Andhra Pradesh PrakasamDistrict523272 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Kidney disease is a significant global health concern, requiring accurate and early diagnosis to prevent progression and ensure timely treatment. This study proposes an improved neural classification logic combining an AutoEncoder and an Artificial Neural Network (ANN) to predict kidney disease using medical image processing techniques. A dataset comprising kidney images from CT, MRI, and ultrasound was utilized to train and test the model. The AutoEncoder was employed for unsupervised feature extraction, reducing dimensionality while retaining key information related to kidney abnormalities. The ANN then classified these features into healthy or diseased categories. image preprocessing techniques such as noise reduction, resizing, normalization, and contrast enhancement were applied to ensure data consistency. Additionally, advanced segmentation algorithms like U-Net were used to isolate the kidney regions from the background, focusing on the Regions of Interest (ROI). Data augmentation was applied to improve generalization. The proposed model achieved a classification accuracy of 94.8%, outperforming baseline models such as CNN (92.1 %) and SVM (88.7%). The AutoEncoder + ANN combination demonstrated superior precision, recall, and Fl-score, with an AUC-ROC of 97.4%. The results highlight the model's robustness and potential for practical applications in the early detection of kidney disease. © 2024 IEEE.

关键词： Noise abatement

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image Enhancement by Various Segmentation Techniques Using Machine Learning 6th

Image Enhancement by Various Segmentation Techniques Using M...

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6th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2021

作者： Chanchlani, Akshita Thakare, Vilas M. Wadhai, Vijay M. Department of Computer Science and Engineering SGBA University Amravati India Department of Electronics and Telecommunications Engineering D. Y. Patil College of Engineering Akurdi Pune India

ISBN: (纸本)9789811935701

Brain Cancer is a disease where abnormal cells grow without control and can invade other tissues, if it is identified at the early stage and treated appropriately then it can save human life. In developing countries cause of disease like brain cancer is effecting many people. Brain Cancer Medical image processing is most important and challenging field now a days. image preprocessing and its segmentation on brain cancer Magnetic Resonance Imaging (MRI) and its analysis is process of finding the objects and understanding their significance. image segmentation have become most popular due to its usage into various applications. The objective of this paper is exploring and implementation of various algorithms of image segmentation on medical image specifically on brain cancer MRI. image segmentation is very important step of understanding the images for further processing and analysis. This paper represents implementation of various image segmentation techniques that includes image Thresholding, Applying Various Morphological Operations, edge and region-based segmentation for enhancement of brain cancer images and detection of contours from brain cancer MRI. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image analysis

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