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About 10,000 papers for “Video processing”

Video processing

Haan, de, G. · Data Archiving and Networked Services (DANS) · 2004

Cited by 0

Video processing.

Rita Laureanti (8107396), Riccardo Barbieri (623266), Luca Cerina (12542509) et al. · Figshare · 2022

<p>In the scheme, the steps applied for the video processing are depicted. Firstly, the ROIs are identified on the face of the subjects and tracked over time. Secondly, a 2-D averaging procedure is applied on the ROIs. Thirdly, a signal cleaning phase follows. Finally, from the thus obtained vPPG signal, the peaks are detected and the HRV parameters computed.</p>

Cited by 0Open access

Video processing

P K Thiruvikraman · 2024

This chapter deals with video processing. We begin with a discussion of a simple MATLAB programme which can extract image frames from a video. This is followed by a discussion on video compression and optical flows.

Cited by 0

Digital video processing

2012

Over the years, thousands of engineering students and professionals relied on Digital Video Processing as the definitive, in-depth guide to digital image and video processing technology. Now, Dr. A. Murat Tekalp has completely revamped the first edition to reflect todays technologies, techniques, algorithms, and trends. Digital Video Processing, Second Edition, reflects important advances in image processing, computer vision, and video compression, including new applications such as digital cinema, ultra-high-resolution video, and 3D video. This edition offers rigorous, comprehensive, balanced, and quantitative coverage of image filtering, motion estimation, tracking, segmentation, video filtering, and compression. Now organized and presented as a true tutorial, it contains updated problem sets and new MATLAB projects in every chapter. Coverage includes Multi-dimensional signals/systems: transforms, sampling, and lattice conversion Digital images and video: human vision, analog/digital video, and video quality Image filtering: gradient estimation, edge detection, scaling, multi-resolution representations, enhancement, de-noising, and restoration Motion estimation: image formation; motion models; differential, matching, optimization, and transform-domain methods; and 3D motion and shape estimation Video segmentation: color and motion segmentation, change detection, shot boundary detection, video matting, video tracking, and performance evaluation Multi-frame filtering: motion-compensated filtering, multi-frame standards conversion, multi-frame noise filtering, restoration, and super-resolution Image compression: lossless compression, JPEG, wavelets, and JPEG2000 Video compression: early standards, ITU-T H.264/MPEG-4 AVC, HEVC, Scalable Video Compression, and stereo/multi-view approaches

Cited by 1,151

VideO Processing Language

Baolin Shao Bs, Huning Dai Hd, Jia Li Jl · 2008

Thanks to the information technology, now we are living a digital life, and gone are the days, when people must have their pictures developed and put into albums. Instead, we began to use digital cameras and to save pictures in a computer years ago, which led to an increasing popularity of picture-editing tools. Currently, however,

Cited by 0

Infrared Video Processing

Törnqvist, Henrik · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2006

An infrared video processing simulator has been implemented in MATLAB to support the development and hardware implementation of image processing algorithms in infrared camera systems. The simulator supports functions like non-uniformity correction, dead-pixel replacement, noise filtering, contrast enhancement and output pixel mapping. Five different methods for image stabilization have been evaluated.

Cited by 0Open access

Video Signal Processing

P. Pirsch, H.-J. Stolberg · Wiley Encyclopedia of Electrical and Electronics Engineering · 1999

Abstract The sections in this article are General Design Approaches for Video Signal Processors Architectural Measures for Programmable Video Signal Processors Design Approaches for Dedicated Architectures Overview of Reported Video Signal Processor Designs Future Trends in Video Signal Processor Design

Cited by 2

Video Processing System

E. G. T. Jaspers, Dsp Cpuasip Asip · 2015

The architecture for block-based video applications (e.g. MPEG/JPEG coding, graphics rendering) is usually based on a processor engine, connected to an external background SDRAM memory where reference images and data are stored. In this paper, we reduce the required memory bandwidth for MPEG coding up to 67 % by identifying the optimal block conguration and applying embedded data compression up to a factor four. It is shown that independent compression of xed-sized data blocks with a xed compression ratio can decrease the memory bandwidth for a limited set of compression factors only. To achieve this result, we exploit the statistical properties of the burst-oriented data exchange to memory. It has been found that embedded compression is particularly attractive for bandwidth reduction when a compression ratio 2 or 4 is chosen. This moderate compression factor can be obtained with a low-cost compression scheme such as DPCM with a small acceptable loss of quality.

Cited by 0

Video processing on SoC

Fracheboud, Loïc, Corthay, François · reroDoc Digital Library · 2020

The goal of this project was to perform real-time video processing tasks on a Zynq SoC with a dedicated development environment embedded in a virtual machine.

Cited by 0

Chapter 4: Video Processing

J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton et al. · Society for Industrial and Applied Mathematics eBooks · 2016

This chapter introduces DMD for robust separation of video frames into background (low-rank) and foreground (sparse) components in real time. The method, as originally conceived by Grosek and Kutz [121], provides a novel application of the DMD technique and its dynamical decomposition for state-of-the-art video processing. DMD modes with Fourier frequencies near the origin (zero modes) are interpreted as background (low-rank) portions of the given video frames, and modes with Fourier frequencies bounded away from the origin constitute their sparse counterparts. An approximate low-rank/sparse separation is achieved at the computational cost of just one SVD and one linear equation solve, producing results orders of magnitude faster than a competing separation method, robust principal component analysis (RPCA). The DMD method developed here is demonstrated to work robustly in real time with personal laptop-class computing power and without any parameter tuning, which is a transformative improvement in performance that is ideal for video surveillance and recognition applications [168, 91].

Cited by 0