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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 4810 条记录，以下是281-290 订阅

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Synergistic image Encryption Using a Hybrid of Logistic, Tent, and Intermittent Chaotic Mappings 2

Synergistic Image Encryption Using a Hybrid of Logistic, Ten...

引用

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

作者： Qu, Yicheng Qin, Zhibao Tsinghua International School Daoxiang Lake Beijing100194 China Communication University of China Beijing100024 China

ISBN: (纸本)9798350331417

The encryption of images is an essential component of ensuring data security in the digital age. Delving into chaotic mappings, our study unveils their robust potential for image encryption. In this paper, we propose a novel scheme for encryption-decryption by merging three chaotic mappings - Logistic, Tent, and Intermittent - into a single scheme. Our rigorous empirical evaluations, spanning histogram uniformity, entropy metrics, and key sensitivity, establish the unparalleled efficacy of our amalgamated approach. © 2023 ieee.

关键词： chaotic mappings image encryptions Intermittent mapping Logistic mapping tent mapping

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A Robustness Study of Machine Learning Based Methods for Macula Detection in Pathological Fundus images 12

A Robustness Study of Machine Learning Based Methods for Mac...

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

作者： Elloumi, Yaroub Kachouri, Rostom ISITCom Hammam-Sousse University of Sousse Medical Technology and Image Processing Laboratory Tunisia Gustave Eiffel University Gaspard Monge Computer Science Laboratory ESIEE-Paris France

ISBN: (纸本)9798350325416

The macula detection is an important step for screening retinal diseases. Several methods have been proposed for detecting macula, with the aim of achieving a higher detection performance despite retinal pathologies. Their processing principles are based on well-known anatomical criteria of macula. However, these criteria are subject to significant changes caused by various eye pathologies. Therefore, it leads to a non-performing macula detection, and hence pathology screening. The objective of this work is to study the robustness of the macula detection methods in terms of each retinal pathologies. For this purpose, the main macular criteria are extracted. Then, recent macula detection methods are detailed where the macula criteria taken into account are identified. Thereafter, the retinal pathologies that affect the macula are listed where caused macula changes are figured out. This study allows us to synthesise the robustness of each method in terms of pathologies. © 2023 ieee.

关键词： Pathology

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Semantically Structured image Compression via Irregular Group-Based Decoupling

Semantically Structured Image Compression via Irregular Grou...

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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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Adding Conditional Control to Text-to-image Diffusion Models

Adding Conditional Control to Text-to-Image Diffusion Models

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ieee/CVF international conference on computer Vision (ICCV)

作者： Zhang, Lvmin Rao, Anyi Agrawala, Maneesh Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9798350307184

We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the productionready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, e.g., edges, depth, segmentation, human pose, etc., with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

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Interactive image Manipulation with Complex Text Instructions 23

Interactive Image Manipulation with Complex Text Instruction...

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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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Comparison of Large Language And Vision Models on Representative Downstream Tasks 2

Comparison of Large Language And Vision Models on Representa...

引用

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

作者： Chen, Huitong Beijing University of Posts and Telecommunications International College Beijing100000 China

ISBN: (纸本)9798350331417

The success of large model pre-training in the fields of natural language processing and computer vision has been remarkable. By pre-training on large-scale data, these models can learn richer semantic and visual representations to achieve better performance on various tasks. performance. In order to enable more researchers and practitioners to fully understand the advantages and applicable fields of different models, this paper summarizes the current large model pre-training methods, including Vision Transformer (ViT), Pre-trained Transformer (GPT), Text-to-Text Transfer Transformer (T5), Bidirectional Encoder Representations from Transformers (BERT) and Contrastive Language-image Pretraining (CLIP) are all included, and discusses their principles and advantages are presented. At the same time, compare their results are compared on different datasets, and compare the results of combining them with different other techniques. The final research results show that ViT has advantages over other models in image classification, GPT performs best in generative tasks compared to other models, and T5 has achieved significant results in various text tasks compared to other models, while BERT performs best in various discriminative tasks compared to other models. CLIP is suitable for image and text-related tasks, including image classification, text retrieval, visual question answering, etc. Finally, applications of various models are summarized, providing direction for researchers and practitioners. © 2023 ieee.

关键词： deep learning large language model large vision model neural network

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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...

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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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Dual-Teacher Color and Structure Distillation for NIR image Colorization

Dual-Teacher Color and Structure Distillation for NIR Image ...

引用

2023 ieee international conference on Visual Communications and image processing, VCIP 2023

作者： Park, Tae-Sung Kim, Jong-Ok Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

Near-infrared (NIR) imaging can acquire more details and textures with less noise in low-light environments compared to RGB. As a result, it has been widely used in low-light vision scenarios such as CCTV, autonomous driving, and military applications. However, applying NIR images to the human cognitive system and computer vision algorithms is challenging due to the lack of color information. Therefore, it is essential to colorize NIR images into RGB ones. We propose a teacher-student deep network with dual-Teacher knowledge distillation to better estimate the original color and structure information. Specifically, our dual-Teacher network is designed to separately teach distinct knowledge, such as color and structure qualities in an image, to the student. Finally, a color guided structure (CGS) and color embedding (CE) module are applied to fuse color and structure features. Our trained model can retain color consistency and detailed structure information of objects. © 2023 ieee.

关键词： Color

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Underwater image processing with Normalized AttUNet

Underwater Image Processing with Normalized AttUNet

引用

2023 international conference on computer, Electronics and Electrical Engineering and their applications, IC2E3 2023

作者： Soorma, Mandeep Singh Chaudhary, Anushka Sonali, Sonali Pal, Sarthak Upadhyay, Dharmendra Kumar Netaji Subhas University of Technology Electronics and Communication Engineering New Delhi Delhi India

ISBN: (纸本)9798350338003

The goal is enhancement of images with different water types and to achieve visual quality improvement of the underwater images. Poor visibility in underwater images is a result of color casts, low contrast, and particle dispersion, all of which are caused by wavelength-dependent and distance-dependent attenuation. To address these degradation issues and to learn water type desensitized features, normalization is applied along both spatial and channel dimensions. In the spatial dimension, instance whitening is applied, to decorrelate activations. Moreover, channel-wise normalization is used to manipulate the first two moments of the channel activations. Then, attention is used to provide weights to certain features based on their importance. We proposed a lightweight model named Normalised AttUNet to provide fast processing time for diverse water type images, while still maintaining comparable PSNR. © 2023 ieee.

关键词： image enhancement

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WSNet: Towards An Effective Method for Wound image Segmentation 23

WSNet: Towards An Effective Method for Wound Image Segmentat...

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

作者： Oota, Subba Reddy Rowtula, Vijay Mohammed, Shahid Liu, Minghsun Gupta, Manish Woundtech Innovat Healthcare Solut Hyderabad Telangana India IIIT Hyderabad Hyderabad Telangana India Microsoft Redmond WA USA

ISBN: (纸本)9781665493468

Medical image segmentation is critical for effective computer-aided diagnosis and localization of ailments. Automated segmentation of wound regions from patient images can aid clinicians in measuring and managing chronic wounds and monitoring the wound healing trajectory. While there exists a plethora of work on general medical image segmentation, there is hardly any work on wound image analysis and segmentation. Existing methods are limited to segmenting a smaller subset of ulcers, such as foot ulcers, with no special processing for wound images. In this paper, we build segmentation models for eight different types of wound images. Wound image analysis is a challenging problem due to the lack of availability of extensive data (labeled or unlabeled), and annotation is also challenging due to the shortage of well-trained wound care clinicians. To handle these challenges, we contribute WOUNDSEG(1), a large and diverse dataset of segmented wound images. Generic wound image segmentation is complex due to the heterogeneous appearance of wound area across images of similar wound types. We propose a novel image segmentation framework, WSNET, which leverages (a) wound-domain adaptive pretraining on a large unlabeled wound image collection and (b) a global-local architecture that utilizes full image and its patches to learn fine-grained details of heterogeneous wounds. On WOUNDSEG, we achieve a decent Dice score of 0.847. On existing AZH Woundcare and Medetec datasets, we establish a new state-of-the-art. Further, we show the impact of using segmentation for improving the accuracy of downstream tasks like wound area and volume prediction.

关键词： image segmentation Wireless sensor networks Adaptation models image analysis computer architecture Wounds Trajectory

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