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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2011"

共 8701 条记录，以下是211-220 订阅

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On real-time Object Recognition by Single image Dehazing Method Using Deep Learning Approach 3

On Real-Time Object Recognition by Single Image Dehazing Met...

引用

3rd International conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Pathak, Shruti Doegar, Amit National Institute of Technical Teachers Training and Research Department of Computer Science Chandigarh160019 India

ISBN: (纸本)9798350330724

In the past decade, various haze removal techniques have been widely reported for object recognition. But hitherto little has been identified on the use of single image dehazing using transfer learning approach for object detection. Single image dehazing is an emerging computer vision technology which offers some of the extreme benefits over the existing techniques such as consumes less processing time, requires less space for real time dehaze purpose, etc. In this study, we combine both the object detection and image dehazing methods for real time applications such as-remote sensing, video surveillances, driverless automatic vehicles, etc. This paper presents an effective and efficient image dehazing method using transfer learning which helps to recognize objects in real time with more clarity and that can automatically detect objects with a high recognition rate and lesser probability of error. Our tests show that object detection becomes less accurate as the haze intensity increases;yet, under all haze circumstances (low, medium, or heavy), our jointly trained model AOD-net +YOLO v3 consistently outperforms non-joint and naïve YOLO v3 techniques. © 2023 IEEE.

关键词： Object detection

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DUALFEAT: DUAL FEATURE AGGREGATION FOR video OBJECT DETECTION 29

DUALFEAT: DUAL FEATURE AGGREGATION FOR VIDEO OBJECT DETECTIO...

引用

IEEE International conference on image processing (ICIP)

作者： Pan, Jing Du, Kaiwen Yan, Yan Wang, Hanzi Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Sch Informat Xiamen Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

video object detection aims to detect and track each object in a given video. However, due to the problem of appearance deterioration in the video, it is still challenging to obtain good results when we apply traditional image object detection methods to videos. In this paper, we propose a new feature aggregation method, called Dual Feature Aggregation (DualFeat) for video object detection. By effectively combining the temporal and spatial attention mechanisms, we make full use of the temporal and spatial information in videos. Meanwhile, we leverage a real-time tracker to track detected objects in video frames, where features are aggregated again with previously obtained features. Such a way helps to obtain more comprehensive and richer features, greatly improving the accuracy of video object detection. We perform experiments on the ILSVRC2017 dataset, and the experimental results also verify the effectiveness of our method.

关键词： video object detection deep learning attention mechanism object tracking

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real-time video enhancement for narrow-band imaging bronchoscopy

Real-time video enhancement for narrow-band imaging bronchos...

引用

2025 conference on Medical Imaging

作者： Daneshpajooh, Vahid Ahmad, Danish Htwe, Yu Toth, Jennifer Bascom, Rebecca Higgins, William E. Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA Penn State Univ Coll Med University Pk PA USA

ISBN: (纸本)9781510685987;9781510685994

Narrow-band imaging (NBI), a relatively new bronchoscopy technology, offers superior visualization of vascular details in lesion areas along the airway walls compared to standard white light bronchoscopy. This empowers physicians to detect suspect lesions and characterize their underlying vascular structures for further indications of cancerous activity. Unfortunately, the bronchoscopic video stream suffers from blurring artifacts due to device and patient motions, resulting in low-resolution visualization of lesion areas. To address this problem, we present an image enhancement method for NBI bronchoscopy to improve: 1) visualization of vascular structures;2) lesion detection;and 3) vessel segmentation. We adapted real-ESRGAN, a single-image super-resolution network, to enhance bronchoscopic images in real-time. This involved a transfer learning approach to fine-tune a pre-trained model using our public NBI bronchial lesion database. The results, derived from bronchoscopic airway exam videos of 10 lung cancer patients, demonstrate significant improvement in the visual quality of super-resolved frames, particularly in vascular regions. Our quantitative analysis further shows enhanced vessel segmentation and lesion detection accuracy, with increased confidence scores. This method offers a practical, real-time solution for improving the diagnostic utility of NBI bronchoscopy by providing clearer, more detailed images. Thus, we integrated the method into an NBI video analysis system for aiding in the early detection and characterization of bronchial lesions.

关键词： airway imaging deep learning image processing bronchoscopy narrow-band imaging lung cancer super-resolution image enhancement

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Enhanced video Streaming Based Computing Encoder for Digital Visual processing 2

Enhanced Video Streaming Based Computing Encoder for Digital...

引用

2nd IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Kumar, Tarun Shukla, Amita Galgotias University Department of Computer Science and Engineering Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Greater Noida India

ISBN: (纸本)9798350347456

The rise of video streaming for digital visual processing has been a boon for the industry of visual processing. video streaming technology has made it easier for companies to capture, analyze, and interpret visual data faster than ever before. It has allowed for the storage and transmission of large amounts of visual data at high speeds, providing businesses with the ability to process and interpret this data in real-time. video streaming technology can be used in a wide variety of applications, including facial recognition, 3D mapping, and object recognition. By streaming video data, companies can quickly and accurately identify individuals, recognize objects, and track movement. This technology can also be used in security applications, such as surveillance and monitoring, as well as in medical imaging, such as MRI and CT scans. video streaming technology has also allowed companies to create more efficient visual processing systems. The streaming video data can be used to automate the process of image recognition and object classification. This has allowed companies to reduce the amount of time and effort needed to interpret visual data. Additionally, streaming video data can be used to create virtual reality experiences, providing users with an immersive experience when viewing digital images. © 2023 IEEE.

关键词： video streaming

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Keyframe Insights into real-time video Tagging of Compressed UHD Content 21st

Keyframe Insights into Real-Time Video Tagging of Compressed...

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21st International conference on image Analysis and processing (ICIAP)

作者： Ruefenacht, Dominic Mobius Labs GmbH Berlin Germany

ISBN: (纸本)9783031064333;9783031064326

We present a method that can analyze coded ultra-high resolution (UHD) video content an order of magnitude faster than real-time. We observe that the larger the resolution of a video, the larger the fraction of the overall processing time is spent on decoding frames from the video. In this paper, we exploit the way video is coded to significantly speed up the frame decoding process. More precisely, we only decode keyframes, which can be decoded significantly faster than 'random' frames in the video. A key insight is that in modern video codecs, keyframes are often placed around scene changes (shot boundaries), and hence form a very representative subset of frames of the video. We show on the example of video genre tagging that keyframes nicely lend themselves to video analysis tasks. Unlike previous genre prediction methods which include a multitude of signals, we train a per-frame genre classification system using a CNN that solely takes (key-)frames as input. We show that the aggregated genre predictions are very competitive to much more involved methods at predicting the video genre(s), and even outperform state-of-the-art genre tagging that solely rely on video frames as input. The proposed system can reliably tag video genres of a compressed video between 12 x (8K content) and 96x (1080p content) faster than real-time.

关键词： Movie genre tagging real-time

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real-time Construction of Stereoscopic video Frames using Three Omnidirectional Cameras 30

Real-Time Construction of Stereoscopic Video Frames using Th...

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30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Cali, Mehmet Gomustekin, Sevket Izmir Yuksek Teknol Enstitusu Elekt Elekt Muhendisligi TR-35430 Izmir Turkey

ISBN: (纸本)9781665450928

There are many studies about acquisition of monoscopic and stereoscopic panorama in the literature. Since obtaining motion parallax accurately without having distortions is a very challenging problem, especially 360-degree stereoscopic image and video capturing has become a prevalent research topic. However, studies in this topic have focused on costly systems with many cameras and high processing power demand. In this study, which presents an efficient solution in terms of processing power and cost, stereoscopic frames are processed in real time using three consumer grade 360-degree cameras whose outputs are sampled according to view orientation. Besides, a method is developed to eliminate the distortions around the borders of the field of view with the help of blending with selected auxiliary camera frames.

关键词： stereoscopic video virtual reality 360-degree video motion parallax

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Scalable video-based Dynamic Mesh Coding

Scalable Video-based Dynamic Mesh Coding

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2023 IEEE International conference on Visual Communications and image processing, VCIP 2023

作者： Byeon, Joohyung Kwon, Naseong Park, Hanje Suh, Jongyeul Sim, Donggyu Kwangwoon University Department of Computer Engineering Seoul Korea Republic of LG Electronics Seoul Korea Republic of

ISBN: (纸本)9798350359855

video-based dynamic mesh compression is being developed for a standard for compressing time-varying mesh data by using existing 2D video codecs. In this paper, we propose a spatial scalable structure for video-based dynamic mesh compression to support scalable applications of the dynamic mesh coding. To support the spatial scalable functionality for geometry, we propose a level of detail based displacement frame packing method that enables decoding up to a certain level. For texture scalable coding, multi-layer texture map is generated from high resolution texture map, and then the scalable high efficiency video coding (SHVC) is employed to remove redundancy between layers. The experimental results demonstrate that our proposed scalable coding method can improve the BD-rate in a 3-layer scalable configuration. © 2023 IEEE.

关键词： Efficiency

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Enhancing video Encoding for Cloud Virtual reality Gaming Based on User Types

Enhancing Video Encoding for Cloud Virtual Reality Gaming Ba...

引用

2023 IEEE International conference on Visual Communications and image processing, VCIP 2023

作者： Song, Kai Sun, Haonan Huo, Junyan Yang, Fuzheng Yang, Kun Chen, Gaoxing Xidian University Xi'an710071 China Alibaba Group Hangzhou311121 China

ISBN: (纸本)9798350359855

Cloud Virtual reality (VR) gaming is a novel technology that allows users to enjoy complex games on their thin clients by offloading the graphics rendering to cloud servers. The thin clients only need to perform basic decoding functions, which reduces the hardware requirements and costs. However, cloud VR gaming also faces the challenge of high bandwidth consumption when transmitting high-resolution game video streams. This paper presents a cloud VR gaming system that can transmit users' gaze point data to the server in real time to identify users' regions of interest. With this system, we verify the difference in spatial visual sensitivity caused by the different types of users. Then, a user-type-based video encoding method is proposed. Through conducting the subjective test experiment, the proposed video encoding method can reduce the bitrate for players and viewers by at least 71% and 69%, respectively, without compromising the perceptual quality. © 2023 IEEE.

关键词： Virtual reality

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Proceedings of the 22nd International conference on image Analysis and processing, ICIAP 2023

Proceedings of the 22nd International Conference on Image An...

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Proceedings of the 22nd International conference on image Analysis and processing, ICIAP 2023

ISBN: (纸本)9783031510229

The proceedings contain 92 papers. The special focus in this conference is on image Analysis and processing. The topics include: An Effective CNN-Based Super Resolution Method for video Coding;medical Transformers for Boosting Automatic Grading of Colon Carcinoma in Histological images;FERMOUTH: Facial Emotion Recognition from the MOUTH Region;consensus Ranking for Efficient Face image Retrieval: A Novel Method for Maximising Precision and Recall;towards Explainable Navigation and Recounting;towards Facial Expression Robustness in Multi-scale Wild Environments;depth Camera Face Recognition by Normalized Fractal Encodings;automatic Generation of Semantic Parts for Face image Synthesis;improved Bilinear Pooling for real-time Pose Event Camera Relocalisation;continual Source-Free Unsupervised Domain Adaptation;End-to-End Asbestos Roof Detection on Orthophotos Using Transformer-Based YOLO Deep Neural Network;OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data;UAV Multi-object Tracking by Combining Two Deep Neural Architectures;GLR: Gradient-Based Learning Rate Scheduler;a Large-scale Analysis of Athletes’ Cumulative Race time in Running Events;uncovering Lies: Deception Detection in a Rolling-Dice Experiment;active Class Selection for Dataset Acquisition in Sign Language Recognition;MC-GTA: A Synthetic Benchmark for Multi-Camera Vehicle Tracking;a Differentiable Entropy Model for Learned image Compression;learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation;self-Similarity Block for Deep image Denoising;SCENE-pathy: Capturing the Visual Selective Attention of People Towards Scene Elements;not with My Name! Inferring Artists’ Names of Input Strings Employed by Diffusion Models;benchmarking of Blind video Deblurring Methods on Long Exposure and Resource Poor Settings;LieToMe: An LSTM-Based Method for Deception Detection by Hand Movements;spatial Transformer Generative Adversarial Network for image Super

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Intelligent video Surveillance using Deep-Learning Models 15

Intelligent Video Surveillance using Deep-Learning Models

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anupama, C.G. Kumar, Gurram Pavan Kumar, D. Chaitanya SRM Institute of Science and Technology Computational Intelligence College of Engineering Chennai India

ISBN: (纸本)9798331300579

Closed-circuit television, or CCTV, is another name for video surveillance. It is a fast-expanding sector that has been around for more than 30 years and has seen many technological advancements. In the modern world, maintaining public safety now requires the use of video surveillance. One can define security. In a number of ways, depending on the situation, including danger of explosion, theft, violence, and so forth. In busy public spaces, "security" can refer to nearly any kind of unusual event. Depending on the user's preferences, intelligent video surveillance records unforeseen activities in homes, offices, and public spaces to provide state-of- the-art smart security. The video surveillance system will actively react to detect actions ahead of time through real-time monitoring and promptly communicate data in the event of an abnormal incident. The main focus is on the application of deep learning techniques to provide motion-activated night vision technology, high-definition picture quality, and tracking of a moving target. Additionally, the system is equipped with automatic audio and visual detection, video recording initiation, and detection of suspicious activities that can activate and alert the systems in any type of weather. The data processing model design aims to employ deep learning technology to visualize data for anomalous activities. It also suggests an intelligent surveillance system to promptly and efficiently identify activities by transmitting a video image and an alert message to the web through realtime processing. Advances in computer vision, especially in deep learning techniques, have created new avenues for these systems to explore, expanding their potential and stimulating new fields of study. © Grenze Scientific Society, 2024.

关键词： Long short-term memory

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