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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2010"

共 8702 条记录，以下是101-110 订阅

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NN-based decision predictor of picture resolution change for video coding 47

NN-based decision predictor of picture resolution change for...

引用

conference on Applications of Digital image processing XLVII

作者： Guermoud, Hassane Bordes, Philippe Galpin, Franck Dumas, Thierry Reuze, Kevin Francois, Edouard Rath, Gagan Tescher, Andrew G. Ebrahimi, Touradj Video Lab InterDigital Res & Innovat 975 Ave Champs Blancs F-35576 Cesson Sevigne France

ISBN: (纸本)9781510679344;9781510679351

Reference Picture Resampling (RPR) is a powerful tool that allows improving video coding efficiency of next generation codecs like Versatile video Coding (VVC) or Enhanced Compression Model (ECM). This feature is well designed to support frame changing resolution without inserting an instantaneous decoder refresh (IDR) or intra random access picture (IRAP). video streaming and low delay scenarios can take advantage of RPR to ensure a smooth frame-based bit-rate adaptation, compared to traditional techniques that can generate bitrate leaps. This paper proposes an encoder method to select the picture resolution change parameters effectively depending on the video signal characteristics. The picture resolution change decision is based on a low complexity neural network, and it is performed before the encoding process without RD-score computations making this approach suitable for real time and low delay implementation. The experiments under Random Access (RA) and All Intra (AI) configurations of the VVC Test Model (VTM-21.1) show that the proposed method can bring luma BD-rate gain improvement of 1.46% and 0.95% respectively compared to the VVC Test Model anchor.

关键词： video Coding Reference Picture Re-sampling Adaptive Resolution Coding Neural Network Versatile video Coding

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SignTrack: Advancements in real-time Sign Language processing for Inclusive Computing with optimized AI 13

SignTrack: Advancements in Real-Time Sign Language Processin...

引用

13th International conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Piperakis, Stavros Papatsimouli, Maria Argyriou, Vasilis Sarigiannidis, Panagiotis Fragulis, George F. Univ Western Macedonia Dept Elect & Comp Engn ZEP Campus Kozani 50100 Greece Kingston Univ Dept Networks & Digital Media Kingston England

ISBN: (纸本)9798350385434;9798350385427

Visual-based translator systems, which utilize image and video data for real-time translation, represent a captivating research area. While previous research has explored text-based translation, limitations exist in capturing the nuances of human communication. Visual-based approaches address this gap by leveraging deep learning and image processing to extract information like objects, faces, and environmental context. This research project investigates the application of deep learning in sign language translation using Automated AI. By analyzing visual data, the system recognizes signs and translates them into spoken languages or generates text descriptions. This technology holds significant promise for various fields - education, entertainment, tourism, and healthcare - and can contribute to the advancement of information technology and artificial intelligence systems.

关键词： Automated AI IoT Sign Language Sign language transcription

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image processing based real-time Online Attendance Monitoring System using Facial Recognition 3

Image Processing based Real-Time Online Attendance Monitorin...

引用

3rd International conference on image processing and Robotics, ICIPRoB 2024

作者： Fernando, Ravishka Athauda, Hashini Cinec Campus Department of Electrical and Electronics Engineering Malabe Sri Lanka

ISBN: (纸本)9798350374766

With the advancement of technology and due to the recent pandemic situation, the education sector has turned to the online teaching method. But the main problem here is the inconvenience and irregularities in the student's attendance. To ensure traditional attendance and reduce time wastage, this research aims to explore and implement an automated attendance marking system using facial recognition technology. This will enable students to be marked present or absent in real-time and will also help teachers to identify students who are present or absent for the online session. As a novelty of this research gives a more efficient and accurate method for attendance marking and eliminating manual and false attendance marking in online sessions. The proposed system employs the latest advancements in image processing and machine learning techniques such as the Haar Cascade feature and LBPH algorithm to accurately detect and recognize the face of a student. The performance of the system is evaluated on its own dataset which was the images of students captured through a video stream from a web camera and the results demonstrate through a confusion matrix its effectiveness in accurately recognizing faces and marking attendance in real-time. The results showed that the attendance system achieved 99.22% accuracy and can accurately mark the attendance of students in an Excel sheet. This real-time GUI-based system is unique in that it revolutionizes the traditional attendance marking process by automating it and providing real-time attendance data. © 2024 IEEE.

关键词： Computer vision

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An FPGA based real-time video processing system on Zynq 7010 2

An FPGA based Real-Time Video Processing system on Zynq 7010

引用

2nd International conference on Advances in Computational Intelligence and Communication, ICACIC 2023

作者： Singha, Antareep Puducherry Technological University Department of Mechatronics Engineering Puducherry India

ISBN: (纸本)9798350318456

real-time image processing involves the transformation of incoming signals, primarily from a camera, into a format that can be readily interpreted by a display device. This process is heavily reliant on precise timing constraints, demanding efficient hardware execution. This paper proposes an innovative method for interfacing the OV7670 Complementary Metal Oxide Semiconductor (CMOS) Camera with an FPGA-based real-time image processing system on a Zynq 7010 platform, using the open-source Digilent Dynamic Clock Generator. The architecture is characterized by it's parallel processing capability of both controlling the camera output signals and processing the signals and converting them from RGB to DVI format on the fly. In lieu of the traditional PLL based clocking wizard, which provides a fixed clock signal, the open-source Dynamic Clock Generator has been incorporated in the architecture to generate the essential pixel clock, meeting the real-time clocking requirements. The RGB to DVI(Digital Visual Interface) block has been coded in VHDL to convert the output from AXI4-Stream to video Out Xilinx IP Core to TMDS (Transition-Minimized Differential Signaling data, to be interpreted by an HDMI compatible monitor. © 2023 IEEE.

关键词： Cameras

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Transfer learning and Machine Learning Classification for Laparoscopic video Distortion Detection 8

Transfer learning and Machine Learning Classification for La...

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8th IEEE International conference on image and Signal processing and their Applications, ISPA 2024

作者： Mohamed, Belmokeddem Kamila, Khemis Salim, Loudjedi University Abou-Bekr Belkaid of Tlemcen Faculty of Technology Biomedical Engineering Department Tlemcen Algeria University Abou-Bekr Belkaid of Tlemcen Surgery B Tlemcen Hospital Department of Medicine Tlemcen Algeria

ISBN: (纸本)9798350309249

Distortions like blur and smoke in real-time laparoscopic videos often result from lens contamination. Detecting these distortions automatically and "in real time"is a step preceding automatic lens cleaning and leads to a clear vision for surgeons, hence reducing surgical time and minimizing risks for patient. Our approach leveraged transfer learning, transposing knowledge from natural images to the laparoscopic video domain. We utilized a pre-trained ResNet50 convolutional neural network (CNN) to extract image features, subsequently processed by a cascade of support vector machine (SVM) classifiers to categorize various distortions. This last strategy amalgamates outputs from two binary classifiers. The first classifier distinguishes videos as good or distorted. The second classifier focuses on smoke and blur detection. The first classifier attains 99 % accuracy. The second classifier achieves 100% accuracy in detecting smoke and blur. These values prove the effectiveness of our approach, which combines ResNet50-based transfer learning and cascaded SVM classification for automatically detecting smoke and blur distortions in laparoscopic videos. Such results are promising for the detection of the remaining distortions. © 2024 IEEE.

关键词： Smoke

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An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

引用

2nd International conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

Computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In Computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning image processing Motion Detection Moving Object Detection YOLO

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Research on Sports Event video Retrieval System Based on Harris Corner Detection Intelligent Algorithm 4

Research on Sports Event Video Retrieval System Based on Har...

引用

4th Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2024

作者： Ma, Yongping Xinjiang University Urumqi830046 China

ISBN: (纸本)9798350359985

USB2.0 is used to design the video retrieval system for sports events. The system uses image sensor as data source, including data transmission, real-time display and corner capture. The detection principle and implementation method based on Harris operator are presented and its performance is analyzed. An image segmentation method based on cumulative frames is proposed, which can recognize different types of images well. Finally, Harris Angle is extracted from the video of various sports games by MATLAB software. In this project, an automatic corner extraction method based on multi-scale features is proposed to solve the problems of missing corner information, position deviation and easy to be disturbed by noise in traditional methods. The experiment shows that the Harris corner extraction technique is fast, consistent and accurate in image segmentation. It has great practical value for the actual image processing. © 2024 IEEE.

关键词： MATLAB

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Low latency immersive content streaming over 5G networks using JPEG XS 47

Low latency immersive content streaming over 5G networks usi...

引用

conference on Applications of Digital image processing XLVII

作者： Rodrigues, Rafael Pinheiro, Antonio M. G. Ebrahimi, Touradj RayShaper SA Valais Switzerland

ISBN: (纸本)9781510679344;9781510679351

With the constant increase in video resolution and frame rate, notably those required for immersive applications, there is a need for efficient and reliable coding technologies suitable for very high visual quality delivery with very low latency. Immersive applications can use the low latency and high bandwidth throughput of 5G networks for increased mobility. JPEG XS is a low-complexity coding standard that can be implemented with very low latency. It is designed to provide visually lossless quality while offering compression efficiency, making it suitable for immersive applications that rely on video content. This paper reports a quality evaluation of omnidirectional video using 360 degrees test sequences coded with JPEG XS. A subjective quality experiment was performed using an alternating double-stimulus method in a VR environment, where subjects could freely commute between the reference and distorted video. Test sequences were encoded at five different bitrates, ranging from 0.35 bpp to 2 bpp. These bitrates are suitable for real-time high-resolution video transmission over 5G networks. It was concluded that JPEG XS provides an effective low-latency solution suitable for high-quality immersive applications using 5G networks.

关键词： Immersive multimedia JPEG XS image and video compression Subjective quality assessment 5G networks

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real-time video Inpainting for RGB-D Pipeline Reconstruction

Real-Time Video Inpainting for RGB-D Pipeline Reconstruction

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Wang, Luyuan Tian, Tina Yan, Xinzhi Ruan, Fujun Aadityaa, G. Jaya Choset, Howie Li, Lu Carnegie Mellon Univ Biorobot Lab Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781665491907

This paper presents a video Inpainting algorithm that enables monocular-camera-laser-based pipeline inspection robots to capture both color and 3D information using only one video stream. Conventional monocular-camera-laser inspection methods are limited to capture either 2D color images or 3D point clouds since the laser tends to overexpose the actual color of the scanning area. We propose a real-time video Inpainting method to solve this problem with minimal hardware needs that can be easily integrated with conventional pipeline profiling robots. The algorithm is accelerated by two components: a lightweight network that directly predicts the complete optical flow and simplifies the algorithm pipeline, and the Polar coordinate transformation, which significantly reduces the image processing compexity. real-world experiments demonstrate that our online algorithm has comparable or better color estimation accuracy against state-of-the-art offline algorithms, while is capable of running at 23 frames per second (FPS) on a laptop computer with a resolution of 1024x1024 pixels. In addition, we verify that this method can be used for video pre-processing for downstream tasks that require high-quality visual inputs, such as Simultaneously Localization and Mapping (SLAM). To the best of our knowledge, this is the first real-time video Inpainting algorithm that can be used for in-pipe environments, serving as an important building block for highly compact RGB-D inspection sensors and robots for the pipeline industry.

关键词： Laptop computers

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EFFICIENT video ENHANCEMENT TRANSFORMER 29

EFFICIENT VIDEO ENHANCEMENT TRANSFORMER

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IEEE International conference on image processing (ICIP)

作者： Vasluianu, Florin Timofte, Radu Univ Wurzburg Comp Vis Lab CAIDAS Wurzburg Germany Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

video Enhancement is an important computer vision task aiming at the removal of the artifacts from a lossy compressed video and the improvement of the visual properties by a photo-realistic restoration of the video contents. Decades of research produced a multitude of efficient algorithms, enabling the reduction of the memory footprint of the transferred video contents in a contiguously increasing network of video streaming services. In this work, we propose VETRAN - a low latency real-time online video Enhancement TRANsformer based on spatial and temporal attention mechanisms. We validate our method on recent video Enhancement NTIRE and AIM challenge benchmarks, i.e. REDS/REDS4, LDV, and IntVID. We improve over the compared state-of-the-art methods both quantitatively and qualitatively, while maintaining a low inference time.

关键词： transformer attention online real-time enhancement

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