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Cross-Device image Saliency Detection: Database and Comparative Analysis

Cross-Device Image Saliency Detection: Database and Comparat...

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IEEE visual communications and image processing (VCIP)

作者： Xiaoying Ding Guanghui Yue Yingxue Zhang School of Information Engineering Zhongnan University of Economics and Law Wuhan China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

While saliency detection for images has been extensively studied during the past decades, only a little work explores the influence of different viewing devices (i.e., tablet computer, mobile phone) towards human visual attention behavior. The lack of research in this field hinders the research progress in cross-device image saliency detection. In this paper, we first establish a novel cross-device saliency detection (CDSD) database based on eye-tracking experiments and investigate subjects’ visual attention behavior when using different viewing devices. Then, we evaluate several classic saliency detection models using the CDSD database and the evaluation results indicate that the cross-device performance of these models need further improvement. Finally, some meaningful discussions are provided which might enlighten the design of cross-device saliency detection model. The proposed CDSD database will be made publicly available.

关键词： Performance evaluation visualization visual communication Tablet computers Gaze tracking Mobile handsets Stability analysis Behavioral sciences visual databases Saliency detection

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249778

The proceedings contain 113 papers. The special focus in this conference is on Applied Technologies. The topics include: Automatic Evaluation of Physiotherapy Activities Using Deep Learning Techniques;multivariable Control Approach Applied to the Embryo Incubation Process of Gallus Gallus Domesticus;discovering visual Deficiencies in Pilots Candidates Using Data Mining;an Electronic Equipment for Measuring Color Difference Between Tissues Based on Digital image processing and Neural Networks;artificial Intelligence-Based Banana Ripeness Detection;U-Net vs. TransUNet: Performance Comparison in Medical image Segmentation;access Control Through Mask Detection and Estimation of People Capacity in Covered Premises;customer Segmentation in Food Retail Sector: An Approach from Customer Behavior and Product Association Rules;Bibliometric Analysis of Web of Science Database STEM Fields in Engineering and Mathematics. Ecuador’s Case Study;smart Antenna Array for Optimal Electromagnetic Energy Capture;image processing Method to Estimate Water Quality Parameter;use of the Student Engagement as a Strategy to Optimize Online Education, Applying a Supervised Machine Learning Model Using Facial Recognition;crime Data Analysis Using Machine Learning Models;Gene Therapy as a Solution to Genetic Diseases Through DNA Manipulation;Vulnerability of CAPTCHA Systems Using Bots with Computer Vision Abilities;military Leadership in the Ecuadorian Army;analysis of Key Variables for Ecuadorian Defense Industry Development;the Prioritization of External Security as a Means of Guaranteeing Multidimensional Security and Economic Growth;Comparative Study of Deep Learning Algorithms in the Detection of Phishing Attacks Based on HTML and Text Obtained from Web Pages;ground Robot for Search and Rescue Management;virtual Training Module for the Production of Rubber Adhesives Through the Production of Cyclopentenol.

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Perceptual Skin Tone Color Difference Measurement for Portrait Photography

Perceptual Skin Tone Color Difference Measurement for Portra...

引用

IEEE visual communications and image processing (VCIP)

作者： Shiqi Gao Huiyu Duan Qihang Xu Jia Wang Xiongkuo Min Guangtao Zhai Patrick Le Callet

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In portrait photography, measuring the perceptual color differences (CDs) of skin tone is significant. Many studies have documented that the perception of skin tone is characteristically different from that of other colors. However, most existing CD measures are proposed based on psychophysical data of uniform color patches or natural images, and do not generalize well to the measurement of skin tone. In this paper, we construct the first large-scale portrait dataset for perceptual skin tone CD assessment and conduct psychophysical experiments to collect 160,000 perceptual CD judgments for 40,000 image triplets. Based on this dataset, we propose a deep skin tone CD measure for portrait photography. Extensive experiments demonstrate that our measure substantially outperforms existing CD measures on the problem of assessing skin tone CDs. The constructed dataset and code will be released to facilitate future research.

关键词： Photography Codes image color analysis visual communication Network architecture Skin Psychophysics

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4th International conference on Applied Technologies, ICAT 2022

4th International Conference on Applied Technologies, ICAT 2...

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4th International conference on Applied Technologies, ICAT 2022

ISBN: (纸本)9783031249846

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Multi-Screen Effects on Quality Assessment: Investigating Banding Metrics Inconsistencies

Multi-Screen Effects on Quality Assessment: Investigating Ba...

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IEEE visual communications and image processing (VCIP)

作者： Nickolay Safonov Dmitriy Kulikov Dmitriy Vatolin Lomonosov Moscow State University Moscow Russia MSU Institute of Advanced Studies of Artificial Intelligence and Intelligent Systems Moscow Russia

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Banding, a common video quality artifact, it refers to the noticeable gradient steps or abrupt transitions between adjacent colors or shades within an image or video, often as a resulting from low color depth or compression algorithms. Assessing banded video quality is vital, especially with the rise of high-quality screens. Several banding metrics have been proposed in recent years to quantify and evaluate the presence of banding artifacts in videos. However, the visibility of banding strongly depends on viewing conditions such as screen brightness, contrast, ambient lighting, and others. This results in inconsistencies in the correlation between banding metrics and the subjective quality perceived across various screens. In this study, we conduct subjective experiments under varied viewing conditions and demonstrate the limited reliability of banding metrics.

关键词： Measurement Performance evaluation Correlation image color analysis visual communication Lighting Quality assessment Reliability Video recording Compression algorithms

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The source picture timing SEI message in the VSEI standard

The source picture timing SEI message in the VSEI standard

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IEEE visual communications and image processing (VCIP)

作者： Sean McCarthy Gary J. Sullivan Peng Yin Dolby Laboratories Inc. San Francisco CA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The source picture timing information (SPTI) supplemental enhancement information (SEI) message indicates to a receiver the actual temporal distance between source pictures in a manner independent from the output timing of corresponding decoded pictures. The SPTI SEI message can also be used to indicate that a coded picture corresponds to a synthetically generated picture rather than an original source picture. The SPTI SEI message has been agreed to be included in the next version of the versatile supplemental enhancement information (VSEI) standard for use with various video coding standards including VVC, HEVC, and AVC. In this paper, the history, syntax, semantics, and several use cases of the SPTI SEI message are described. Example use cases include high-speed imaging, variable-rate source pictures, event-driven image capture, time-lapse video, time-reverse video, frame rate conversion, motion analysis, and machine analysis for scientific, industrial, and forensic applications.

关键词： image coding visual communication Forensics Semantics Imaging Receivers Syntactics Timing Motion analysis Standards

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image Forensics Strikes Back: Defense Against Adversarial Patch

Image Forensics Strikes Back: Defense Against Adversarial Pa...

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IEEE visual communications and image processing (VCIP)

作者： Ching-Chia Kao Chun-Shien Lu Chia-Mu Yu Institute of Information Science (IIS) Academia Sinica ROC National Taiwan University ROC National Yang Ming Chiao Tung University ROC

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Traffic sign recognition plays a crucial role in self-driving cars, but unfortunately, it is vulnerable to adversarial patches (AP). Although AP can efficiently fool DNN-based models in previous studies, the connection between image forensics and AP detection still needs to be explored. From a high-level point of view, their goals are the same. That is to find tampered regions and prevent false positives in the meantime. A natural question arises: "Is achieving application-agnostic anomaly detection possible?" In this paper, we propose image Forensics Defense Against Adversarial Patch (IDAP), a framework to defend against adversarial patches via generalizable features learned from tampered images. In addition, we incorporate the Hausdorff erosion loss into our network model for joint training to complete the shape of a predicted mask. Extensive experimental comparisons on three datasets, including COCO, DFG, and APRICOT demonstrate that IDAP outperforms state-of-the-art AP detection methods.

关键词： Training image forensics visual communication Shape Predictive models Feature extraction Autonomous automobiles Anomaly detection Erosion

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Enhancing Intra Block Copy Prediction for Plenoptic 2.0 Video Coding under Macropixel Constraints

Enhancing Intra Block Copy Prediction for Plenoptic 2.0 Vide...

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IEEE visual communications and image processing (VCIP)

作者： Vinh Van Duong Thuc Nguyen Huu Jonghoon Yim Byeungwoo Jeon Department of Electrical and Computer Engineering Sungkyunkwan University Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In this paper we introduce a novel approach to better utilize the intra block copy (IBC) prediction tool in encoding lenslet light field video (LFV) captured using plenoptic 2.0 cameras. Although the IBC tool has been recognized as promising for encoding LFV content, its fundamental limit due to its original design rooted for encoding conventional videos suggests slight modification possibility to better suit the property of LFV content. Observing the inherently large amount of repetitive image patterns due to the microlens array (MLA) structure of plenoptic cameras, several techniques are suggested in this paper to enhance the IBC coding tool itself for more efficiently encoding LFV contents. Our experimental results demonstrate that the proposed method significantly enhances the IBC coding performance in case of encoding LFV contents while concurrently reducing encoding time.

关键词： Video coding image coding visual communication Cameras Light fields Microoptics Lenses

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Teledermatologist: Automated Diagnosis of Skin Diseases with image Recognition

Teledermatologist: Automated Diagnosis of Skin Diseases with...

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IEEE visual communications and image processing (VCIP)

作者： Nadav Ishai Dolev Peretz Natalia Vanetik Marina Litvak Department on Software Engineering Shamoon College of Engineering Beer Sheva Israel

Early diagnosis of skin diseases is crucial for effective treatment, preventing spread, minimizing long-term damage, managing chronic conditions, detecting underlying health issues, and promoting psychological well-being. It is often necessary to do difficult tests in order to obtain a diagnosis, which demands a lot of resources from patients as well as medical personnel. Motivated by this shortage, we introduce a user-friendly and private system for the automatic detection of skin disease from a picture. Our system employs a deep neural model for the accurate classification of a given picture to one of the predefined classes of diseases. We created a large dataset of six skin lesion types and tested different neural models. The best-performing model, which achieved 92% accuracy, was integrated into our system. The application and its web demo can be accessed at https://***/.

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A Diverse Environment Coal Gangue image Segmentation Model Combining Improved U-Net and Semi-supervised Automatic Annotation 8th

A Diverse Environment Coal Gangue Image Segmentation Model C...

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8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Liu, Xiuhua Zhu, Wenbo Zhu, Zhengjun Luo, Lufeng Zhang, Yunzhi Lu, Qinghua School of Mechatronic Engineering and Automation Foshan University Foshan528000 China China Coal Technology Engineering Group Tangshan Research Institute Tangshan063000 China

ISBN: (纸本)9789819980208

The problem of uneven illumination in the coal gangue image and the existence of fine coal gangue reduces the accuracy of the coal gangue location, which also makes it difficult to the annotation of the coal gangue images. In this paper, we propose an improved U-Net gangue segmentation algorithm to achieve gangue sorting and automatic image annotation under diversified environments. Specifically, to solve the problem that some fine coal and gangue fragments are difficult to be recognized, the InceptionV1 module is introduced instead of some of these convolutional blocks. Secondly, to solve the visual blur caused by light changes in industrial sites, the CPAM attention module is added to the fusion part of U-Net. Finally, to alleviate the difficulty of coal gangue image annotation, a semi-supervised human-in-the-loop automatic annotation framework is constructed, and a statistical method based on classification probability is proposed to automatically screen the questionable labels. The experimental results show that the proposed improved segmentation model exhibits good performance in the coal gangue sorting task with MIoU values of 80.23% and MPA values of 86.16%, which have higher accuracy compared to the original model. In addition, the statistical method proposed in this study can effectively screen out the incorrect and correct labeled data, thus enabling automatic annotation and screening. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image annotation

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