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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4812 条记录，以下是291-300 订阅

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Interactive image Manipulation with Complex Text Instructions 23

Interactive Image Manipulation with Complex Text Instruction...

引用

23rd ieee/CVF Winter conference on applications of computer Vision (WACV)

作者： Morita, Ryugo Zhang, Zhiqiang Ho, Man M. Zhou, Jinjia Hosei Univ Tokyo Japan

ISBN: (纸本)9781665493468

Recently, text-guided image manipulation has received increasing attention in the research field of multimedia processing and computer vision due to its high flexibility and controllability. Its goal is to semantically manipulate parts of an input reference image according to the text descriptions. However, most of the existing works have the following problems: (1) text-irrelevant content cannot always be maintained but randomly changed, (2) the performance of image manipulation still needs to be further improved, (3) only can manipulate descriptive attributes. To solve these problems, we propose a novel image manipulation method that interactively edits an image using complex text instructions. It allows users to not only improve the accuracy of image manipulation but also achieve complex tasks such as enlarging, dwindling, or removing objects and replacing the background with the input image. To make these tasks possible, we apply three strategies. First, the given image is divided into text-relevant content and text-irrelevant content. Only the text-relevant content is manipulated and the text-irrelevant content can be maintained. Second, a super-resolution method is used to enlarge the manipulation region to further improve the operability and to help manipulate the object itself. Third, a user interface is introduced for editing the segmentation map interactively to re-modify the generated image according to the user's desires. Extensive experiments on the Caltech-UCSD Birds-200-2011 (CUB) dataset and Microsoft Common Objects in Context (MS COCO) datasets demonstrate our proposed method can enable interactive, flexible, and accurate image manipulation in real-time. Through qualitative and quantitative evaluations, we show that the proposed model outperforms other state-of-the-art methods.

关键词： computer vision Semantic segmentation Computational modeling Superresolution Process control User interfaces Controllability

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Semantically Structured image Compression via Irregular Group-Based Decoupling

Semantically Structured Image Compression via Irregular Grou...

引用

ieee/CVF international conference on computer Vision (ICCV)

作者： Feng, Ruoyu Gao, Yixin Jin, Xin Feng, Runsen Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Eastern Inst Technol Ningbo Peoples R China

ISBN: (纸本)9798350307184

image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applications. To overcome this limitation, some previous works propose to semantically structure the bitstream, which can meet specific application requirements by selective transmission and reconstruction. Nevertheless, they divide the input image into multiple rectangular regions according to semantics and ignore avoiding information interaction among them, causing waste of bitrate and distorted reconstruction of region boundaries. In this paper, we propose to decouple an image into multiple groups with irregular shapes based on a customized group mask and compress them independently. Our group mask describes the image at a finer granularity, enabling significant bitrate saving by reducing the transmission of redundant content. Moreover, to ensure the fidelity of selective reconstruction, this paper proposes the concept of group-independent transform that maintain the independence among distinct groups. And we instantiate it by the proposed GroupIndependent Swin-Block (GI Swin-Block). Experimental results demonstrate that our framework structures the bitstream with negligible cost, and exhibits superior performance on both visual quality and intelligent task supporting.

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One Shot Face image Style Transfer Method Based on GAN 2

One Shot Face Image Style Transfer Method Based on GAN

引用

2nd international conference on image processing, computer Vision and Machine Learning, ICICML 2023

作者： He, Wenyin Wuhan University Department of Computer Science Hubei Wuhan430072 China

ISBN: (纸本)9798350331417

Style transformation on face images has traditionally been a popular research area in the field of computer vision, and its applications are quite extensive. Currently, the more mainstream schemes include Generative Adversarial Network (GAN)-based image generation as well as style transformation and Stable diffusion method. In 2019, the NVIDIA team proposed StyleGAN, which is a relatively mature scheme for generating real faces as well as face feature blending. The whole StyleGAN model is trained based on the Flickr-Faces-HQ Dataset (FFHQ) dataset, the This is a large dataset, so the model takes a long time to train. My aim is to form a One-shot stylized face image generator, which means that only one reference face and one stylized face need to be input, and a brand-new face with a mixture of features can be generated in a short training time. This is inspired by the existing research result JoJoGAN, which learns a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN's style-mixing property to produce a substantial paired dataset from a single example of the style. This paper will make improvements to JoJoGAN, including improving the encoder that utilizes the GAN Inversion method to generate latent codes for image features, and the random mixing of latent codes to produce a more refined paired dataset. © 2023 ieee.

关键词： deep learning GAN style transformation

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Research on the Innovation System of computer Artificial Intelligence Technology in Museum Financial Management System 3

Research on the Innovation System of Computer Artificial Int...

引用

3rd ieee international conference on Power, Electronics and computer applications, ICPECA 2023

作者： Wang, Siyu Yunnan University Yunnan Kunming China

ISBN: (纸本)9781665472784

This paper designs a museum financial management system based on computer artificial intelligence technology. The system introduces RPA technology to automatically collect, identify and classify image data of image recognition, speech recognition and original documents to help improve the efficiency of voucher entry and audit. This paper focuses on the general ledger management certificate of museum financial sharing service center to optimize the design of financial process automation. The system realizes the concentration and integration of business and financial activities through the process reengineering of financial fund settlement, accounting, budget preparation and financial analysis of the museum, so as to improve the quality of financial information disclosure, budget management and internal control efficiency of the museum. © 2023 ieee.

关键词： image enhancement

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Execution Time Analysis of Multithreading and Multiprocessing on Seam Carving Algorithm

Execution Time Analysis of Multithreading and Multiprocessin...

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2023 international conference on Advances in Intelligent Computing and applications, AICAPS 2023

作者： Joshi, Aditya Deepak Bhattacharyay, Rajarshi Luhadia, Garvit Vijayarajan, V. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350333817

The objective of this research is to demonstrate the performance difference exhibited by the seam carving algorithm when executed sequentially on a conventional CPU as opposed to when it is run parallelly. Multithreading and multiprocessing were two of the parallelization methods used in this project. These execution times were recorded, and an execution time analysis was performed. In the end, the researchers compared the two to determine which parallelization technique performed better. The researchers believe that the applications of this algorithm when parallelized can be significantly expanded. This expansion could potentially allow it to be used in applications such as thumbnail generation, responsive web development, and a complete contentaware alternative to image resizing (skewing and stretching). © 2023 ieee.

关键词： image processing

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image Captioner: Captioning for Visual Impact 1

Image Captioner: Captioning for Visual Impact

引用

1st ieee international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Aryan, Shabareesh Subrahmanya, S. Jagtap, Pahel Mani, Leander Sowmya, M.R. Nitte Meenakshi Institute of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350316469

image captioning is the process of generating a textual description that accurately represents the features of an image. In the realm of deep learning, this task is of utmost significance and has a wide range of uses. image captioning involves converting an image, represented as a sequence of pixels, into a sequence of words that are relevant to the image. It can be seen as an end-to-end and sequence-to-sequence challenge, as both the language and visual aspects need to be processed. In this regard, recurrent neural networks are employed for the language processing, while convolutional neural networks are used to extract feature vectors from the images. The findings of this study highlight the effectiveness of this method and show how diverse applications in image processing and description generation are possible. This work promotes the incorporation of these methods into actual image captioning systems in order to provide more precise and contextually appropriate image descriptions. © 2023 ieee.

关键词： BLEU: Bilingual Evaluation Understudy convolutional neural networks image captioning long short-term memory pooling recurrent neural networks unsupervised learning

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Identifying color space for improved image display 12

Identifying color space for improved image display

引用

12th international conference on image processing Theory, Tools and applications, IPTA 2023

作者： Togban, Elvis Kerouh, Fatma Ziou, Djemel Université de Sherbrooke Faculté des Sciences Département d'Informatique Sherbrooke Canada

ISBN: (纸本)9798350325416

Color reproduction is important in many tasks such as images display on the web, photography and textile industry. Ideally, the data about the color space in which an image is encoded can be found in the metadata. Given that information, a display system can convert the image in the appropriate color space for a faithful reproduction. However, when the metadata is missing or damaged, the image will be misrepresented. In this paper, we focus on the identification of RGB color space. Five color spaces are considered namely sRGB, ColorMatch RGB, Adobe RGB, Apple RGB and ProPhoto RGB. We exploit the correlation between the pixels as features. © 2023 ieee.

关键词： Metadata

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A deep-learning based algorithm for image reconstruction in Compton tomography 12

A deep-learning based algorithm for image reconstruction in ...

引用

12th international conference on image processing Theory, Tools and applications, IPTA 2023

作者： Ayad, Ishak Tarpau, Cecilia Cebeiro, Javier Nguyen, Mai K. Cy Cergy Paris Université Etis Umr 8051 and Agm Umr 8088 Cergy-Pontoise France Heriot-Watt University Edinburgh School of Mathematical and Computer Sciences United Kingdom Comision de Investigaciones Cientificas CIC Iteca UNSAM-CONICET Provincia de Buenos Aires Argentina Cy Cergy Paris Université Etis Umr 8051 Cergy-Pontoise France

ISBN: (纸本)9798350325416

In this paper we study the applications of deep-learning to the problem of image reconstruction in Compton scatter tomography, a field where deep-learning techniques are still unexplored. Particularly, we focus on a new design with uncollimated detectors that simplifies some previous configurations of Compton scanners. The system inherits attractive advantages such as non-moving components and the ability to combine with other imaging modes. Since there is no an analytic inverse formula for image reconstruction, we developed a GAN based algorithm that provides an efficient mapping between data and image domains. We compare our method against several algorithmic approaches and show that high quality image reconstruction is feasible. Results encourage further research in the application of deep-learning reconstruction techniques in Compton scatter tomography, particularly when inverse reconstruction formulas are unknown. © 2023 ieee.

关键词： image reconstruction

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Keys to Better image Inpainting: Structure and Texture Go Hand in Hand 23

Keys to Better Image Inpainting: Structure and Texture Go Ha...

引用

23rd ieee/CVF Winter conference on applications of computer Vision (WACV)

作者： Jain, Jitesh Zhou, Yuqian Yu, Ning Shi, Humphrey Univ Oregon SHI Lab Eugene OR 97403 USA IIT Roorkee Roorkee Uttar Pradesh India Picsart AI Res PAIR Roorkee Uttar Pradesh India Adobe Inc San Jose CA USA Salesforce Res San Francisco CA USA

ISBN: (纸本)9781665493468

Deep image inpainting has made impressive progress with recent advances in image generation and processing algorithms. We claim that the performance of inpainting algorithms can be better judged by the generated structures and textures. Structures refer to the generated object boundary or novel geometric structures within the hole, while texture refers to high-frequency details, especially man-made repeating patterns filled inside the structural regions. We believe that better structures are usually obtained from a coarse-to-fine GAN-based generator network while repeating patterns nowadays can be better modeled using state-of-the-art high-frequency fast fourier convolutional layers. In this paper, we propose a novel inpainting network combining the advantages of the two designs. Therefore, our model achieves a remarkable visual quality to match state-of-the-art performance in both structure generation and repeating texture synthesis using a single network. Extensive experiments demonstrate the effectiveness of the method, and our conclusions further highlight the two critical factors of image inpainting quality, structures, and textures, as the future design directions of inpainting networks.

关键词： Visualization computer vision Statistical analysis image synthesis Computational modeling Generators Periodic structures

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A Comparative Analysis of image Captioning Techniques 4

A Comparative Analysis of Image Captioning Techniques

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4th international conference for Emerging Technology, INCET 2023

作者： Sunil, Diya Theresa Safar, Seema Das, Abhijith Amijith, M.M. Joshy, Devika M. Rajagiri School of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350335750

image captioning is the task of generating a textual description that accurately represents the content of an image. This task involves combining computer vision techniques, such as object recognition and scene understanding, with natural language processing to produce a human-like description of an image. Over time, various models have been introduced to perform image captioning, all aiming to accurately describe the content of an image. These models have practical applications such as improving the accessibility of multimedia content, assisting individuals with visual impairments, medical image captioning, and enhancing image search and retrieval. This paper explores some of the models and studies their efficiency using different evaluation metrics. © 2023 ieee.

关键词： Object recognition

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