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检索条件"任意字段=Proceedings of the 3rd International Conference on Video and Image Processing"

共 12962 条记录，以下是41-50 订阅

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Does display size affect short-term memory tasks? 8

Does display size affect short-term memory tasks?

引用

8th international conference on video and image processing, ICVIP 2024

作者： Harada, Yohei Ishikawa, Tomokazu Toyo University 1-7-11 Akabanedai Kita-ku Tokyo Japan Prometech CG Research 3-34-3 Hongo Bunkyo-ku Tokyo Japan

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Today, a wide variety of electronic devices are in use, such as smartphones, tablets, televisions, and monitors. Studies have been conducted to determine what the optimal display size is for various tasks. In this study, an experiment on display size and memory retention will be conducted and evaluated, including the results of a questionnaire. For the experiment, we created two types of questions based on memory sports events, and prepared a total of five different displays (smartphone, tablet, and three types of monitors). The results of the experiment showed that the mean scores for both of the two memory tasks were lowest for the smartphone and highest for the 31.5-inch monitor, but the analysis showed no clear significant differences by display size. However, taking into account the results of the questionnaire, we could find that the 31.5" monitor was relatively easy to remember, while the 6.1" smartphone tended to be harder to remember. © 2025 SPIE.

关键词： Photons

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3rd international Workshop on Cancer Prevention, detection and intervenTion, CaPTion 2024, held in Conjunction with 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Cancer Prevention, detection a...

引用

3rd international Workshop on Cancer Prevention, detection and intervenTion, CaPTion 2024, held in Conjunction with 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031733758

The proceedings contain 22 papers. The special focus in this conference is on Cancer Prevention, detection and intervenTion. The topics include: Classification of Endoscopy and video Capsule images Using CNN-Transformer Model;multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases;seeing More with Less: Meta-learning and Diffusion Models for Tumor Characterization in Low-Data Settings;performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification;on Undesired Emergent Behaviors in Compound Prostate Cancer Detection Systems;optimizing Multi-expert Consensus for Classification and Precise Localization of Barrett’s Neoplasia;Automated Hepatocellular Carcinoma Analysis in Multi-phase CT with Deep Learning;Refining Deep Learning Segmentation Maps with a Local Thresholding Approach: Application to Liver Surface Nodularity Quantification in CT;Uncertainty-Aware Deep Learning Classification for MRI-Based Prostate Cancer Detection;Generalized Polyp Detection from Colonoscopy Frames Using Proposed EDF-YOLO8 Network;AI-Assisted Laryngeal Examination System;UltraWeak: Enhancing Breast Ultrasound Cancer Detection with Deformable DETR and Weak Supervision;SelectiveKD: A Semi-supervised Framework for Cancer Detection in DBT Through Knowledge Distillation and Pseudo-labeling;AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients;deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery;treatment Efficacy Prediction of Focused Ultrasound Therapies Using Multi-parametric Magnetic Resonance Imaging;SurRecNet: A Multi-task Model with Integrating MRI and Diagnostic Descriptions for Rectal Cancer Survival Analysis;improved Prediction of Recurrence After Prostate Cancer Radiotherapy Using Multimodal Data and in Silico simulations;autoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for Radiotherapy

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Fire Detection and Autonomous Evacuation Guidance System 3

Fire Detection and Autonomous Evacuation Guidance System

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vadlamudi, Sandyarani Roja Ramani, D. Preetham, S. Dagadi, Ganesh Goyal, Nipun Mantri, Harshavardhan V. Department of AIML New Horizon College of Engineering Bengaluru India Department of CSE New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

Effective fire emergency responses are critical to ensuring safety and minimizing damage. This research exhibits an innovative fire detection and evacuation system that utilizes deep-learning and real-time video analysis to improve response times and efficiency. Utilizing convolution neural nets (CNNs), the system is able to detect fires with high precision and minimal delay, while dynamically generating evacuation plans depending on the fire's location. The system integrates advanced machine learning techniques to analyse building layouts and determine the safest, shortest evacuation routes. This information is conveyed to occupants via a public address system, enabling a streamlined and well-coordinated evacuation process. By combining real-time fire detection with intelligent route optimization, the system offers a significant advancement over traditional fire safety technologies. The paper also addresses key challenges such as processing latency and the system's ability to operate effectively in real-world conditions. Experimental results demonstrate the reliability of the CNN model—Inception V3 for fire recognition and Dijkstra algorithm for route planning—in both accuracy and speed aspects. The system’s ability to handle complex visual data while maintaining operational efficiency is a highlight of this research. Along with technical details, the research explores the practical implications of implementing this system in various environments. Key challenges include maintaining low latency in video streaming and fire detection, as well as ensuring the effectiveness of the escape route analysis in complex environments. Future improvements focus on scalability, with plans to expand the system’s capabilities to support multi-camera setups and larger buildings, making it adaptable for widespread use. © 2025 IEEE.

关键词： video streaming

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Data processing system design methodology for high-orbit remote sensing satellites 3

Data processing system design methodology for high-orbit rem...

引用

2024 3rd international conference on Aerospace and Control Engineering, ICoACE 2024

作者： Li, Yongfeng Hao, Xiuqiang Zhang, Lang Xi’an710100 China

Aiming at the characteristics of high orbit remote sensing satellite, this paper proposes a data processing system design method that adopts a number of key technologies such as the highly reliable data interface, high fidelity image compression, multi-file storage management, adaptive rate control, etc., and realizes the error-free code exchange of high-speed remote sensing data among multiple devices with lower hardware resource overhead, has better image compression performance, and supports several tasks to be carried out in parallel without increasing additional hardware resources, and enables the effective frame efficiency of the transmission channel to reach 100%. The method proposed in this paper efficiently realizes the data processing and transmission requirements of high-orbit remote sensing satellites and significantly improves the application efficiency of high-orbit remote sensing satellites. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： image compression

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processing landslide hazard prediction using satellite image segmentation 3

Processing landslide hazard prediction using satellite image...

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3rd international conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Chiu, Chenyuan Hsu, Sung-Chi Huang, Yishuo Zhang, Nan Peng, Chen Labib Ul Islam, Syed School of Civil Engineering Jiaying University Guangdong Meizhou514015 China Dept. of Construction Engineering Chaoyang Univ. of Technology 168 Jifong East Rd. Wufong Taichung41349 Taiwan School of Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Dept. of Civil Engineering University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

ISBN: (纸本)9781510689077

Remote sensing technology has become one of the indispensable techniques for the investigation of geologic disasters at home and abroad. However, the interpretation of bare ground classification by satellite multispectral images is often interfered by atmospheric clouds in mountainous areas, shadows on the mountains, and the phenomenon of Speckle Signals, which seriously affects the correctness of the interpretation images, and is prone to errors such as misjudgment or omission. In this study, the SPOT 5 satellite images of the study area were segmented by applying the Multiphase Level Set Framework using MATLAB computation. The results show that the problem of the speckle signals can be eliminated efficiently and quickly, and the complex terrain and features can be segmented into simple objects, which can solve the problem of the spatial recognition rate without being affected by the resolution of the image itself, and improve the image accuracy. This can solve the problem of spatial recognition without being affected by the resolution of the image itself, and improve the accuracy of satellite image interpretation, which can be used as a possible landslide trend. © 2025 SPIE.

关键词： Landslides

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A K-means Algorithm for Feature Extraction in Electrochemical Determination of Nanocomposites 3rd

A K-means Algorithm for Feature Extraction in Electrochemica...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Lin, Junyuan Qu, Shiyu Xi ‘an Gaoxin No. 1 High School International Class Xi ‘an China Shaanxi Open University Xi ‘an China

ISBN: (纸本)9789819791231

Nanocomposites offer a unique material choice for enhancing sensor sensitivity and stability in electrochemical measurements. Nowadays, the feature extraction and image processing of nanocomposites in electrochemical determination have serious problems of error rate and low efficiency, which have a great influence on the experimental process. K-means algorithm is mostly used for image feature extraction. In this paper, K-means algorithm is used to extract and process images of nanocomposites in electrochemical determination effectively, and conclusions are drawn through experiments. The algorithm greatly reduces the error rate of feature extraction and image processing, and the accuracy rate is increased to 99.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nanoclay

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Dreamvideo: High-Fidelity image-to-video Generation with image Retention and Text Guidance

DreamVideo: High-Fidelity Image-to-Video Generation with Ima...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wang, Cong Gu, Jiaxi Hu, Panwen Guo, Yuanfan Dong, Xiao Xu, Hang Liang, Xiaodan School of Intelligent Engineering Sun Yat-sen University at Shenzhen Shenzhen China Noah'ark Lab Huawei Shanghai China School of Science and Engineering The Chinese University of Hong Kong at Shenzhen Shenzhen China School of Intelligent Engineering Sun Yat-sen University at Zhuhai Zhuhai China

ISBN: (纸本)9798350368741

image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods frequently integrate semantic information from images or simply concatenate images, which often leads to low fidelity and flickering in the generated videos. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named Dreamvideo. Our Dreamvideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating the designed double-condition classifier-free guidance, Dreamvideo can generate high-quality videos of different actions by providing varying prompt texts. We conduct comprehensive experiments on the public datasets, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method. © 2025 IEEE.

关键词： computer vision controllable video generation deep learning diffusion model image-to-video generation

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Depth-Aware Weather-Enhanced 3D Object Detection for Roadside Infrastructure 8

Depth-Aware Weather-Enhanced 3D Object Detection for Roadsid...

引用

8th international conference on video and image processing, ICVIP 2024

作者： Li, Yanfei Zeng, Yuan Gong, Yi Department of Electronics and Electrical Engineering Southern University of Science and Technology China College of Big Data and Internet Shenzhen Technology University China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Monocular 3D object detection is a crucial topic in autonomous driving and Intelligent transportation systems (ITS). Most existing methods are evaluated on clean datasets but exhibit arresting performance degradation in the real world with varying weather conditions. image enhancement methods have been introduced in existing vehicle-centric works to address the impact of diverse weather on detection performance and improve the robustness of the model. Unlike vehicle-centric systems, roadside infrastructure can enlarge perception range and is less affected by vehicle occlusion to increase the response time in case of danger. To enhance the robustness of detection models on roadside infrastructure, this work introduces an image enhancement method incorporating depth information in modeling climate changes. We use a classification network trained on real weather data to validate the effectiveness of the enhancement and use state-of-the-art roadside infrastructure monocular detection models to evaluate the effectiveness of our method on object detection. Extensive experiments demonstrate that our method can enhance the robustness and generalization of detection models under various weather conditions. © 2025 SPIE.

关键词： Object detection Rain image enhancement Data modeling Education and training Cameras 3D modeling Adverse weather Performance modeling image contrast enhancement

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Optimization of the integrated video surveillance system with elements of data analysis 3

Optimization of the integrated video surveillance system wit...

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3rd international conference on Cyber Hygiene and Conflict Management in Global Information Networks, CH and CMiGIN 2024

作者： Odarchenko, Roman Pevnev, Vladyslav Pinchuk, Alla Polihenko, Oleh National Aviation University Liubomyra Huzara Ave. 1 Kyiv03058 Ukraine

The video surveillance market is one of the most popular areas in information technology. Therefore, the question of creating an effective approach to the construction of video surveillance systems, a method of optimizing subsystems and their correct adaptation, which was solved in this work, is acute. To solve this problem, the task was decomposed into user groups and software and hardware levels, existing tools in the field of application and their analysis were identified. An analysis of new and unpopular tools was carried out to check the expediency of their use. Thus, several main levels of the video surveillance system were created, each of which had separate tools for solving problems. The following levels are defined: equipment selection, solution topology, network optimization, use of data processing hardware resources, and visualization of system operation. Each of these sections carries a global solution to problems, namely, reducing financial costs, reducing the number of equipment, eliminating unprofitability, thereby ensuring the use of a smaller number of environmental resources for specific tasks and saving the time and efficiency of the work of specialists. As a result, a method of optimizing integrated video surveillance systems was developed for two blocks of users: the administrator and the operator. The method is modular, so its further development and filling with new technologies is possible in the future. © 2025 Copyright for this paper by its authors.

关键词： Security systems

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Shape-Guided Unsupervised 3D Defect Detection 8

Shape-Guided Unsupervised 3D Defect Detection

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8th international conference on video and image processing, ICVIP 2024

作者： Yuan, Yizhao Guo, Zhiwan Guan, Jiahao Xi’an Jiaotong University Xi’an710049 China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

We propose a shape-guided deep learning framework to address unsupervised anomaly detection problems. Our method is suitable for scenarios with large data volume, long execution time, and sufficient hardware conditions. To address the problems of insufficient feature extraction ability and poor detail feature expression in 3D detection methods based on symbolic distance functions, we propose a shape-guided 3D defect detection deep learning method. We designed the PointNet-P module, which enhances the model’s feature extraction ability by implementing a multi-scale feature extraction mechanism and improving a mixed attention mechanism. We also designed a neural implicit function module, which enhances the neural implicit function’s detail feature expression ability by incorporating Fourier encoding and adding a sine loss function. We used the AdamW loss function to improve overfitting. Our method effectively achieves state-of-the-art performance on the MVTec 3D-AD dataset. © 2025 SPIE.

关键词： Unsupervised learning

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