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检索条件"任意字段=Conference on Visual Communications and Image Processing"

共 3458 条记录，以下是391-400 订阅

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Context-Aware Seam Restoration for image Extension

Context-Aware Seam Restoration for Image Extension

引用

IEEE visual communications and image processing (VCIP)

作者： Yuk Heo Yeong Jun Koh Chang-Su Kim Korea University Seoul South Korea Chungnam National University Daejeon South Korea

A seam is a set of pixels with minimum energy forming a continuous line in an image. By eliminating or duplicating seams iteratively, an input image can be retargeted. However, this process often results in blurring, stretching, or distortion problems around the seams, especially when extending a target image. We propose a novel approach for image extension using content-aware seam restoration to solve this problem. First, we design CSR-Net, which employs features from the horizontal region of target pixels to restore the seams. Second, we develop an image extension scenario based on the seam restoration and the training methodology of CSR-Net. Experimental results demonstrate that the proposed algorithm provides more accurate expanded results at seam pixels the seams than conventional algorithms.

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Codec-agnostic Lenslet Video Coding with Smoothing Transform

Codec-agnostic Lenslet Video Coding with Smoothing Transform

引用

IEEE visual communications and image processing (VCIP)

作者： Eline Soetens Gauthier Lafruit Mehrdad Teratani Laboratory of Image Synthesis and Analysis Université Libre de Bruxelles Brussels Belgium

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Plenoptic cameras are light field capturing devices able to acquire large amounts of angular and spatial information. The lenslet video produced by such cameras presents on each frame a distinctive hexagonal pattern of micro-images. Due to the particular structure of lenslet images, traditional video codecs perform poorly on lenslet video. Previous works have proposed a preprocessing scheme that cuts and realigns the micro-images on each lenslet frame. While effective, this method introduces high frequency components into the processed image. In this paper, we propose an additional step to the aforementioned scheme by applying an invertible smoothing transform. We evaluate the enhanced scheme on lenslet video sequences captured with single-focused and multi-focused plenoptic cameras. On average, the enhanced scheme achieves 9.85% bitrate reduction compared to the existing scheme.

关键词： Video coding Performance evaluation Smoothing methods visual communication Bit rate Video sequences Transforms Cameras Light fields Video codecs

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Fully Aligned Network for Referring image Segmentation

Fully Aligned Network for Referring Image Segmentation

引用

IEEE visual communications and image processing (VCIP)

作者： Yong Liu Ruihao Xu Yansong Tang Tsinghua Shenzhen International Graduate School Tsinghua University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper focuses on the Referring image Segmentation (RIS) task, which aims to segment objects from an image based on a given language description, having significant potential in practical applications such as food safety detection. Recent advances using the attention mechanism for cross-modal interaction have achieved excellent progress. However, current methods tend to lack explicit principles of interaction design as guidelines, leading to inadequate cross-modal comprehension. Additionally, most previous works use a single-modal mask decoder for prediction, losing the advantage of full cross-modal alignment. To address these challenges, we present a Fully Aligned Network (FAN) that follows four cross-modal interaction principles. Under the guidance of reasonable rules, our FAN achieves state-of-the-art performance on the prevalent RIS benchmarks (RefCOCO, RefCOCO+, G-Ref) with a simple architecture.

关键词： image segmentation Fans visualization Attention mechanisms image coding visual communication Benchmark testing Food safety Decoding Guidelines

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Gait Recognition with DensePose Energy images 28th

Gait Recognition with DensePose Energy Images

引用

28th International conference on Systems, Signals and image processing (IWSSIP)

作者： Schwarz, Philipp Scharinger, Josef Hofer, Philipp Johannes Kepler Univ Linz LIT Secure & Correct Syst Lab Linz Austria Johannes Kepler Univ Linz Inst Computat Percept Linz Austria Johannes Kepler Univ Linz Inst Networks & Secur Linz Austria

ISBN: (纸本)9783030968786;9783030968779

In the recent years, special emphasis has been placed on visual-based gait recognition due to its unique characteristics such as not requiring a special user action, or its long-distance recognizability. In general, there exist two methods - model-based and appearance-based methods - both of which come with their own advantages and disadvantages. In an effort to harness the best of both worlds we create a compact 3D human model-based gait representation out of 2D images with the help of the DensePose algorithm. We design a simple CNN and train several instances to show that the obtained gait representation can in fact be used to improve gait recognition accuracy. Experimental results are based on the publicly available CASIA-B dataset.

关键词： Biometrics Gait recognition DensePose Energy image

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visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping 5

Visual Data Enciphering via DNA Encoding, S-Box, and Tent Ma...

引用

5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Gabr, Mohamed Younis, Hana Ibrahim, Marwa Alajmy, Sara Alexan, Wassim German Univ Cairo Fac MET Cairo Egypt German Univ Cairo Fac IET Cairo Egypt

ISBN: (纸本)9781665462198

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

关键词： Chaotic maps cryptography image encryption Mersenne Twister S-box Tent map

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Fidelity-preserving Learning-Based image Compression: Loss Function and Subjective Evaluation Methodology

Fidelity-preserving Learning-Based Image Compression: Loss F...

引用

IEEE visual communications and image processing (VCIP)

作者： Shima Mohammadi Yaojun Wu João Ascenso Instituto Superior Técnico Instituto de Telecomunicações Bytedance

Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion between the decoded and the original ground truth images. This paper focuses on perceptual optimization of learning-based image compression solutions and proposes: i) novel loss function to be used during training and ii) novel subjective test methodology that aims to evaluate the decoded image fidelity. According to experimental results from the subjective test taken with the new methodology, the optimization procedure can enhance image quality for low-rates while offering no advantage for high-rates.

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Deep Learning: A Detailed Analysis of Various image Augmentation Techniques 1

Deep Learning: A Detailed Analysis of Various Image Augmenta...

引用

1st IEEE International conference on Networking and communications, ICNWC 2023

作者： Swathi, S. Rajalakshmi, M. Senniappan, Vijayalakshmi Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Coimbatore Institute of Technology Department of Information Technology Coimbatore India Park College of Engineering Tech Department of Information Technology Coimbatore India

ISBN: (纸本)9798350336009

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses. © 2023 IEEE.

关键词： Computer vision

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Improved Wild Horse Optimizer with Deep Learning Model for Skin Lesion Detection and Classification on Dermoscopic images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

引用

23rd International conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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Privacy Protection for image Sharing Using Reversible Adversarial Examples

Privacy Protection for Image Sharing Using Reversible Advers...

引用

IEEE International conference on communications (ICC)

作者： Ping Yin Wei Chen Jiaxi Zheng Yiting Zhang Lifa Wu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Online image sharing on social media platforms faces information leakage due to deep learning-aided privacy attacks. To avoid these attacks, this paper proposes a privacy protection mechanism for image sharing without changing the visual effect, which is based on reversible adversarial examples. Specifically, social media platform users can change the class activation feature to convert the original image into an adversarial image before sharing. When users want to restore the adversarial image to the original image, they can use an improved generative adversarial network model to restore it. The experimental results prove that the conversion model in this paper can effectively prevent privacy attacks from analyzing and stealing users' private information while having no visual impact. At the same time, the proposed restoration model can restore the adversarial examples with high accuracy.

关键词： Training Privacy image recognition Social networking (online) Perturbation methods visual effects Information leakage

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MVBind: Self-Supervised Music Recommendation for Videos via Embedding Space Binding

MVBind: Self-Supervised Music Recommendation for Videos via ...

引用

IEEE visual communications and image processing (VCIP)

作者： Jiajie Teng Huiyu Duan Yucheng Zhu Sijing Wu Guangtao Zhai Shanghai Jiao Tong University China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent years have witnessed the rapid development of short videos, which usually contain both visual and audio modalities. Background music is important to the short videos, which can significantly influence the emotions of the viewers. However, at present, the background music of short videos is generally chosen by the video producer, and there is a lack of automatic music recommendation methods for short videos. This paper introduces MVBind, an innovative Music-Video embedding space Binding model for cross-modal retrieval. MVBind operates as a self-supervised approach, acquiring inherent knowledge of intermodal relationships directly from data, without the need of manual annotations. Additionally, to compensate the lack of a corresponding musical-visual pair dataset for short videos, we construct a dataset, SVM-10K (Short Video with Music-10K), which mainly consists of meticulously selected short videos. On this dataset, MVBind manifests significantly improved performance compared to other baseline methods. The database and code are available at: https://***/IntMeGroup/MVBind.

关键词： visualization Codes Databases visual communication Annotations image processing Complexity theory Recommender systems Videos Cross modal retrieval

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