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Research on image and text in visual communication design under artificial intelligence

Research on image and text in visual communication design un...

引用

2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Yuan, Yuanyi Wu, Longjun Zou, Yong Chengdu Technological University Chengdu China

ISBN: (纸本)9781510680425

The paper examines the integration of AI in visual communication design, particularly focusing on the enhanced impact of images when combined with text. It explores the evolution of visual communication design in the context of AI advancements, emphasizing the increasing significance of this discipline in today's technology-driven society. Key areas of discussion include the role of artificial intelligence, specifically deep learning, in image recognition and natural language processing, and its application in visual communication design. The paper details the development and training of an image recognition and text matching model, showcasing the effective application of convolutional neural networks (CNNs) and Word2Vec in visual design tasks. The research concludes by highlighting the successful translation of deep learning theoretical achievements into practical visual communication design applications, demonstrating the model's proficiency in image recognition and text matching within a business and engineering framework. © 2024 SPIE.

关键词： Convolutional neural networks

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Optical image processing technologies using generalized connectivity W-spectrum 20

Optical image processing technologies using generalized conn...

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conference on Optical Fibers and their applications

作者： Tymchenko, Leonid Kokriatska, Natalia Tverdomed, Volodymyr Braikovskyi, Mykhailo Stetsenko, Oleksandr Voitovych, Olesia Semenova, Liudmyla Kolesnyk, Irina Smolarz, Andrzej Rakhmetulina, Saule Kumargazhanova, Saule State Univ Infrastruct & Technol Kyiv Ukraine Vinnytsia Natl Tech Univ Vinnytsia Ukraine Lublin Univ Technol Lublin Poland Serikbayev East Kazakhstan Tech Univ Ust Kamenogorsk Kazakhstan

ISBN: (纸本)9781510672895;9781510672888

This paper describes an approach to solving the problem of fast pattern recognition with image co-ordinate detection and measurement under undefined noise and signal conditions. An analysis of the use of the W-transform method as a basis for image comparison algorithms was carried out. image comparison algorithms with noise robustness were developed.

关键词： W-spectrum correlation optical image comparison W-transformation

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On the impact of learning-based image compression on computer vision tasks 47

On the impact of learning-based image compression on compute...

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conference on applications of Digital image processing xLVII

作者： Akamatsu, Shunsuke Testolina, Michela Upenik, Evgeniy Ebrahimi, Touradj Waseda Univ Adv Multimedia Syst Lab Shillman Hall 4013-14-9 OkuboShinjuku Ku Tokyo 1690072 Japan Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. While most of the research to date has focused on image coding for humans, JPEG AI plans to address both human and machine vision by presenting several non-normative decoders addressing multiple image processing and computer vision tasks in addition to standard reconstruction. While the impact of conventional image compression on computer vision tasks has already been addressed, no study has been conducted to assess the impact of learning-based image compression on such tasks. In this paper, the impact of learning-based image compression, including JPEG AI, on computer vision tasks is reviewed and discussed, mainly focusing on the image classification task along with object detection and segmentation. This study reviews the impact of image compression with JPEG AI on various computer vision models. It shows the superiority of JPEG AI over other conventional and learning-based compression models.

关键词： JPEG AI learning-based image compression computer vision image classification object detection

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Gabor wavelets in Face Recognition and Its applications 1

引用

International conference on Intelligent Emerging Methods of Artificial Intelligence and Cloud Computing, IEMAICLOUD 2021

作者： Ojha, Manoj Department of Electronics and Communication Engineering Sanskriti University Uttar Pradesh Mathura India

ISBN: (数字)9783030929053

ISBN: (纸本)9783030929046

Face recognition has become an advanced area in the field of recognition and has various applications especially in biometrics, forensic investigations, smart advertising, national database for identity cards etc. Various methods and algorithms are available for face recognition using image processing, machine vision and deep learning. In image processing, wavelets transform is one of the effective methods as they are capable of extracting both spatial and frequency information of the given signal and is a substitute of Fourier transforms in many applications of signal processing used in machine vision, speech analysis etc. Among all the wavelet transforms the most efficient is Gabor wavelet transform that is widely used in research done in the field of image processingas it has certain important mathematical properties. Gaborwavelet transform is also used as they yield distortion tolerance space for various recognitions including face recognition and pattern recognition. This paper addresses the applications of Gabor wavelets in face recognition along with face reconstruction, iris recognition, and corner detection etc. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Benchmarking conventional and learning-based objective image quality metrics on compression artifacts 47

Benchmarking conventional and learning-based objective image...

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conference on applications of Digital image processing xLVII

作者： Testolina, Michela Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

Evaluating the visual quality of distorted images is a crucial task in the field of image compression, as artifacts may significantly impair the appeal and fidelity of images, therefore reducing the user experience. As assessing the visual quality of images through subjective visual quality experiments is often not feasible, objective image quality metrics are considered to be very attractive alternatives. Traditional objective image quality metrics, such as the Peak signal-to-Noise Ratio and Structural Similarity Index, have long been used to assess compression artifacts. However, due to the complexity of human perception, estimated objective visual quality scores often diverge from their subjective counterparts. Recent advancements in deep learning have led to the development of learning-based metrics that promise to estimate the perceived visual quality of images with better accuracy. While learning-based methods have demonstrated enhanced performance compared to conventional methods on a number of datasets, their generalization performance across different quality ranges and artifacts has not been assessed yet. This paper presents a benchmarking study of conventional and learning-based objective image quality metrics while focusing solely on image compression artifacts. The experimental framework includes five source images with various contents compressed with five legacy and recent compression algorithms at four different quality levels, specifically focusing on the high-quality range. Results indicate that, in many cases, learning-based metrics present a higher correlation with human visual perception when compared to conventional methods, highlighting the potential of integrating such metrics in the development and refinement of image compression techniques.

关键词： Visual quality objective image quality metrics image compression deep-learning benchmarking

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Low Complexity image Inpainting Using AutoEncoder 5

Low Complexity Image Inpainting Using AutoEncoder

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5th International conference on Communications, signal processing, and their applications (ICCSPA)

作者： Elbehery, Abeer Fahmy, Yasmine Kafafy, Mai Cairo Univ Fac Engn Giza 12613 Egypt

ISBN: (纸本)9781665482370

image inpainting is filling the missing or corrupted pixels in an image in a realistic way that cannot be differentiated by human eye. Deep learning is widely used in image inpainting and it exhibits better performance than classical inpainting methods, but it requires high processing resources and longer time to train the model. In this paper, we propose an autoencoder architecture that outperforms other deep learning techniques in literature methods with lower processing and time complexity.

关键词： image inpainting Autoencoder Skip connections Low complexity Adam optimizer

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Investigation of the Theory and applications of Deep Learning-based image Recognition

Investigation of the Theory and Applications of Deep Learnin...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Cai, Likai Beijing-Dublin International College Beijing University of Technology Beijing Beijing100000 China

ISBN: (纸本)9781510666351

image recognition technology, as an important branch in the field of artificial intelligence, aims to process and analyze images to recognize objects in images. The aim of this paper is to summarize the general approach to implementing image recognition and the application of this technology in some fields by analyzing relevant articles in the field of image recognition in recent years and to suggest possible conjectures on the future direction of this technology. This paper points out that the image recognition process is divided into two main parts: image processing and image recognition. For the implementation of the corresponding algorithms, this paper introduces two commonly used software libraries: OpenCV and YOLOv5. In addition, the paper also focuses on the applications and contributions of image recognition techniques to face recognition, lane detection, and medical image analysis. It is also pointed out that image recognition technology has a very promising future, and that there is still a lot of further improvement in the field of medical image analysis. © 2023 SPIE.

关键词： Noise abatement

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A METHOD FOR x-RAY image LANDMARKS LOCALIZATION USING CYCLIC COORDINATE-GUIDED STRATEGY 49

A METHOD FOR X-RAY IMAGE LANDMARKS LOCALIZATION USING CYCLIC...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Wang, xianglong An, xifeng Rigalll, Eric Zhang, Shu Yu, Hui Dong, Junyu Ocean Univ China Qingdao Peoples R China Univ Portsmouth Portsmouth Hants England

ISBN: (纸本)9798350344868;9798350344851

In this study, we present a novel method for pinpointing landmarks in x-ray images, which simultaneously offers computational efficiency and localization precision. Our method leverages a cyclic coordinate-guided strategy that requires fewer model parameters and lower computational costs than traditional heatmap-based supervised methods. This is crucial for medical imaging applications where imaging devices often have limited computational resources yet require high-precision landmark localization. Our methodology involves a two-stage process that employs cyclic inference to optimize landmark localization. In the first stage, non-uniform sampling is used to capture the multi-scale features of landmarks. This is followed by a second stage in which cyclic training fine-tunes the landmark coordinates towards their optimal positions. Our results indicate that our two-stage process achieves competitive localization performance with state-of-the-art methods yet with added benefits of lower computational overhead and smaller parameter count. Additionally, a global block was developed to capture global position information of landmarks, and experiments showed its effectiveness and its contribution in enhancing the model's landmark localization accuracy. We validated our method using two publicly available datasets, and the source code for our experiments is available on GitHub: https://***/switch626/***.

关键词： x-ray images landmark localization deep learning cyclic coordinate-guided

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Online-learned Graph Transforms for Adaptive Blocksize Intra-Predictive Coding 47

Online-learned Graph Transforms for Adaptive Blocksize Intra...

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conference on applications of Digital image processing xLVII

作者： Lu, Wen-Yang Pavez, Eduardo Ortega, Antonio Zhao, xin Liu, Shan Univ Southern Calif Dept ECE Los Angeles CA 90007 USA Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781510679344;9781510679351

Current video coding standards, including H.264/AVC, HEVC, and VVC, utilize discrete cosine transform (DCT), discrete sine transform (DST), to decorrelate the intra-prediction residuals. However, these transforms often face challenges in effectively decorrelating signals with complex, non-smooth, and non-periodic structures. Even in smooth areas, an abrupt transition (due to noise or prediction artifacts) can limit their effectiveness. This paper presents a novel block-adaptive separable path graph-based transform (GBT) that is particularly adept at handling such signals. This new method focuses on adaptively modifying the block size and learning GBT to enhance the performance. The GBT is learned in an online scenario using sequential K-means clustering, where each available block size has K clusters and K GBT kernels. This approach allows the GBT to be dynamically learned for the current block based on previously reconstructed areas with same block size and similar characteristics. Our evaluation, integrating this method with H.264/AVC intra-coding tools, shows significant improvement over the traditional H.264/AVC DCT in processing high-resolution natural images.

关键词： Graph signal processing graph-based transform signal-dependent transform online learning transform coding intra-prediction

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Storing images and point clouds on DNA support with fountain codes 47

Storing images and point clouds on DNA support with fountain...

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conference on applications of Digital image processing xLVII

作者： Lazzarotto, Davi Ramos, Jorge Encinas Testolina, Michela Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG Lausanne Switzerland

ISBN: (纸本)9781510679344;9781510679351

The use of DNA molecules as a storage medium has been recently proposed as a solution to the exponentially increasing demand for data storage, achieving lower energy consumption while offering better lifespan and higher information density. The nucleotides composing the molecules can be regarded as quaternary symbols, but constraints are generally imposed to avoid sequences prone to errors during synthesis, archival, and sequencing. While the majority of previous works in the field have proposed methods for translating general binary data into nucleotides, others have presented algorithms tailored for specific data types such as images as well as joining source and channel coding into a single process. This paper proposes and evaluates a method that integrates DNA Fountain codes with state-of-the-art compression techniques, targeting the storage of images and three-dimensional point clouds. Results demonstrate that the proposed method outperforms previous techniques for coding images directly into DNA, putting forward a first benchmark for the coding of point clouds.

关键词： DNA-based archival image compression Point cloud compression Fountain codes

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