TOFlow: Video Enhancement with Task-Oriented Flow
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Updated
Nov 11, 2019 - MATLAB
TOFlow: Video Enhancement with Task-Oriented Flow
Robust Local Optical Flow (RLOF)
fft/ifft transformations, DCT encoding/decoding using various techniques (zig-zag scanning, quantization), SNR calculations, filters (Gaussian, median, bilateral), edge detection, motion assessment, moving objects detection, JPEG model study
This repository is about video compression, and more specifically about the motion estimation block (ME block) of a video encoder. It is a research project for developing an efficient motion estimation algorithm, so that the video compression technology can keep pace with the high frame rate videos and high resolution videos.
Based on extracting heart beat using Eulerian Video Magnification technique for amplifying temporal changes in video
img2vid
Matlab implementation of the CS video reconstruction method RRS
Generating a short summarized video output of a input video strem using clustering
A Matlab code for tracking colloidal fluorescent nanoparticles.
Extracting contrast-filled vessels in X-ray angiography by graduated RPCA with motion coherency constraint
Uniform Color Space based HDR video compression: This repository contains the code for a novel HDR video compression algorithm which has been proposed to compress HDR video frames to codec suitable YUV files which can be compressed using 10-bit video codecs (x264/x265/AV1)
Eliminate defects in ancient film footage
An implementation of timelapse effect using video synopsis
Inter-frame motion calculation using inter-frame differencing and optical flow estimation
Video and Image Encryption Tool Written in MATLAB
Image and Video Processing with MATLAB.
Tracking and analysis of a moving object on a 2-Dimensional space (video) using Kalman filter Algorithm
Multiple Face Detection tool using MATLAB. It implements tracking multiple objects in real time using WebCam and Kanade-Lucas-Tomasi (KLT) algorithm. It automatically detects and tracks multiple faces in a webcam-acquired video stream.
Source code for the NIPS 2018 paper https://nips.cc/Conferences/2018/Schedule?showEvent=11183
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