Most video restoration networks are slow, have high computational load, and can't be used for real-timevideo enhancement. In this work, we design an efficient and fast framework to perform real-timevideo enhance...
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In this paper, we introduce an effective technique for real-time motion classification using event cameras to process input data streams. Our method allows for real-time operation. To enhance memory efficiency, our ap...
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Constructing photo-realistic Free-Viewpoint videos ( FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advance-ments achieved by current neural rendering techniques,...
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ISBN:
(纸本)9798350353006
Constructing photo-realistic Free-Viewpoint videos ( FVVs) of dynamic scenes from multi-view videos remains a challenging endeavor. Despite the remarkable advance-ments achieved by current neural rendering techniques, these methods generally require complete video sequences for offline training and are not capable of real-time render-ing. To address these constraints, we introduce 3DGStream, a method designed for efficient FVV streaming of real-world dynamic scenes. Our method achieves fast on-the-fly per-frame reconstruction within 12 seconds and real-time rendering at 200 FPS. Specifically, we utilize 3D Gaussians ( 3DGs) to represent the scene. Instead of the naive ap-proach of directly optimizing 3DGs per-frame, we employ a compact Neural Transformation Cache (NTC) to model the translations and rotations of 3DGs, markedly reducing the training time and storage required for each FVV frame. Furthermore, we propose an adaptive 3DG addition strat-egy to handle emerging objects in dynamic scenes. Exper-iments demonstrate that 3DGStream achieves competitive performance in terms of rendering speed, image quality, training time, and model storage when compared with state-of-the-art methods.
With the increasing prevalence of chronic health conditions and the growing trend of telemedicine, there is a rising demand for reliable and non-invasive remote health monitoring solutions. The proposed paper aims to ...
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ISBN:
(纸本)9798331522667
With the increasing prevalence of chronic health conditions and the growing trend of telemedicine, there is a rising demand for reliable and non-invasive remote health monitoring solutions. The proposed paper aims to develop a real-time health monitoring system by leveraging advanced signal processing techniques and computer vision through webcam integration. The system focuses on the extraction and analysis of physiological signals, particularly those derived from facial features using CNN, to monitor and assess health parameters such as heart rate. The system employs a robust signal processing pipeline that includes color extraction, normalization, rendering, interpolation, and Fourier transformation (FFT) to analyze the periodicity of signals captured from facial regions of interest (ROIs). These signals, primarily focusing on the green color channel, are indicative of blood flow and can be used to estimate heart rate. Additionally, a Butterworth bandpass filter is applied to refine the signal, ensuring that only the relevant frequency components are retained for accurate analysis. The core of the project is a computer vision system that captures real-timevideo input from a webcam, processes each frame to extract the necessary facial regions using CNN, and applies the aforementioned signal processing techniques to monitor physiological health indicators. The system is designed to function autonomously, requiring minimal user intervention, and provides real-time feedback on the user's health status. By integrating signal processing with computer vision, this project aims to create an accessible and non-invasive tool for continuous health monitoring, which can be extended to applications in remote healthcare, fitness tracking, and wellness monitoring. From this above content we can declare that the heart rate prediction will be declared by the correct estimation and through references each reference will be declared as a correct and positive value though this refe
Diffusion models have demonstrated remarkable capabilities in text-to-image and text-to-video generation, opening up possibilities for video editing based on textual input. However, the computational cost associated w...
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At present, in the analysis task of hundreds of video streams, the general processing method is to pull the video streams to the data center and apply a graphics processing unit (GPU) cluster for centralized analysis ...
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video restoration is a widely studied task in the field of computer vision and imageprocessing. The primary objective of video restoration is to improve the visual quality of degraded videos caused by various factors...
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Diffusion models have obtained substantial progress in image-to-video generation. However, in this paper, we find that these models tend to generate videos with less motion than expected. We attribute this to the issu...
This paper proposes the design of videoimage hardware structure processing based on FPGA. This improves the processing speed and real-time performance of the intelligent automatic assembly pipeline inspection system....
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imageprocessing has become extremely important, with the consequences of real-timeimageprocessing failures being severe;thus, research and study in real-timeimageprocessing methods are extremely important. Some i...
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