cross-correlation

There are 90 repositories under cross-correlation topic.

  • seanwood/gcc-nmf

    Real-time GCC-NMF Blind Speech Separation and Enhancement

    Language:Python3111216133

  • eqcorrscan/EQcorrscan

    Earthquake detection and analysis in Python.

    Language:Python1641923586
  • PIVlab

    Shrediquette/PIVlab

    Particle Image Velocimetry for Matlab, official repository

    Language:MATLAB123129930

  • alopatindev/sync-audio-tracks

    Audio tracks synchronization command-line tool for video editors that don't support it

    Language:C++59569
  • SeisNoise.jl

    JuliaSeismo/SeisNoise.jl

    Ambient Noise Cross-Correlation in Julia

    Language:Julia5263218

  • YijianZhou/MESS

    Matched filter earthquake detector

    Language:Python331117

  • chaddy1004/sobel-operator-pytorch

    Sobel edge detection implemented on PyTorch

    Language:Python32227

  • forughi/PIV

    A Particle Image Velocimetry (PIV) code in Python and Matlab

    Language:Python293316

  • H-K-R/CSE3132-Digital-Signal-Processing-Lab

    This repository will contain necessary signal processing codes in Matlab or Python of my course " Digital Signal Processing (CSE3132) ".

    Language:MATLAB24104

  • OpenSMFS/pycorrelate

    Fast and accurate cross-correlation over arbitrary time lags.

    Language:Python22567

  • IvanGCh/NDCP

    Noisy Dispersion Curve Picking

    Language:MATLAB213013

  • GKalliatakis/Fourier-Analysis-Digital-Images

    Fourier analysis applications for image matching.

    Language:Matlab19209

  • robertarvind/Interactive-Semi-Automatic-Image-2D-Bounding-Box-Annotation-Tool-using-Multi-Template_Matching

    Interactive Semi Automatic Image 2D Bounding Box Annotation and Labelling Tool using Multi Template Matching An Interactive Semi Automatic Image 2D Bounding Box Annotation/Labelling Tool to aid the Annotater/User to rapidly create 2D Bounding Box Single Object Detection masks for large number of training images in a semi automatic manner in order to train an object detection deep neural network such as Mask R-CNN or U-Net. As the Annotater/User starts annotating/labelling by drawing a bounding box for a few number of images in the selected folder then the algorithm suggests bounding box predictions for the rest of the yet to be annotated/labelled images in the folder. If the predictions are right then the user/annotater can simply press the keyboard key 'y' which indicates that the detected bounding box is correct. If the prediction is wrong then the user/annotater can manually draw a rectangular 2D bounding box over the correct ROI (Region of interest) in the image and then press the key 'y' to proceed further to the rest of the images in the folder. If the user/annotater made a mistake while drawing the 2D bounding box, then he/she can press the key 'n' in order to remove the incorrectly marked 2D bounding box and he/she can repeat the process for the same image until he/she draws the correct 2D bounding box and then after drawing the correct 2D bounding box, the user/annotater may press the key 'y' to continue to the rest of the images. The 2D bounding box prediction over the whole image data set improves as the user/annotater annotates/labels more number of images by drawing 2D bounding boxes. This tool allows the user/annotater to not only interactively and rapidly annotate large number of images but also to validate the predictions at the same time interactively. This tool helps the user/annotater to save a lot of time when annotating/labelling and validating the predictions for a large number of training images in a folder. Instructions to use:- 1. If the training images are in JPEG or any other format, then convert them to PNG format using some other tool or program before using these images for annotation. 2. All the training images must contain the object of interest which is to be annotated. 3. Currently the application only supports 2D bounding box annotation for single object detection per image, but in the future semantic segmentation based annotation features will be added which will allow precise boundary segmentation masks of an object in an image. 4. If some or all of the training images have varying dimensions(shapes/resolutions), then resize them to the same dimensions using this tool by providing the height and width to which all the training images need to be resized to. The height and width are inputed separately in two different dialog boxes which pop up once the program is executed. If the training images need not be resized then press the cancel button in the dialog boxes requesting the height and width. 5. Select the folder containing the training images by navigating to the folder containing the training images through a dialog box which pops up after the program is executed. If the images need to be resized then two dialog boxes pop up. The first dialog box is to navigate to the destination folder containing the unresized raw training images and after resizing another dialog box pops up to navigate to the folder containing the saved resized training images named as "resized_data". If the images need not be resized then only one dialog box pops up so that the user can navigate to the raw training images folder directly. 6. The images in the folder pop up one by one. After drawing the correct 2D bounding box over the ROI (region of Interest), press the 'y' key. Except the first image, the rest of the images will have a 2D bounding box drawn over them. If the predicted box is accurate, then continue by pressing the 'y' key. If the prediction is incorrect, then draw the accurate bounding box and press the 'y' key. If any mistake occured while drawing the 2D box, then reset the image by removing the incorrect drawing by pressing the 'n' key and then draw the correct box and press the 'y' key. 7. The output images are stored in four different folders in the same directory containing the training images folder. among the four folders, one contains the cropped templates of the bounding boxes, black and white mask images, training images and the images with 2D box detection markings.

    Language:Python19214

  • SaiferGit/Digital-Signal-Processing-Codes

    This is a repository where I added my DSP codes that have written in Matlab (without built-in function). I have also commented inside every code so that it will become helpful for newbies. Also added cross-check using Library Function.

    Language:MATLAB17104

  • vidify/old-audiosync

    First implementation of the audio synchronization feature for Vidify, now obsolete

    Language:C172413

  • swiss-seismological-service/scdetect

    A computationally efficient earthquake detection module for SeisComP

    Language:C++157656

  • core-man/SeisCC

    Seismic cross-correlation codes

    Language:C10103

  • zlogic/cybervision

    Cybervision can generate a 3D model from two photos of an object

    Language:Rust10201

  • asuszko/signal_cross_correlation

    Custom CUDA kernel doing a normalized cross correlation on a batch of signals via pycu_interface.

    Language:Python9001

  • imodpasteur/Brightfield-Drift-Correction-3D

    Acquire bright field images along with the super resolution data and use it to track drift in 3D with nanometer precision!

    Language:Python9820

  • 5a7man/eeg_fConn

    Python library to compute functional connectivity measures from EEG

    Language:Python8102

  • CNelias/DCCA.jl

    Julia module for Detrended Cross-Correlation Analysis.

    Language:Julia8203

  • freemocap/skelly_synchronize

    Synchronization tool for videos of the same event. Uses audio cross correlation to synchronize.

    Language:Python8340

  • NoiseCIEI/ANCC

    Seismic Ambient Noise Two-Station Interferometry

    Language:C8201

  • NoiseCIEI/Seed2Cor

    Seismic Ambient Noise Cross-Correlation in Parallel

    Language:C++8201

  • AEBilgrau/correlateR

    General purpose correlation and covariance estimation

    Language:R6462

  • ErichZimmer/Fluidity

    A Python GUI for Digital Particle Image Velocity (DPIV)

    Language:Python6284

  • fwolling/PulSync

    PerHealth'21 - PulSync: The Heart Rate Variability as a Unique Fingerprint for the Alignment of Sensor Data Across Multiple Wearable Devices

    Language:Jupyter Notebook6100

  • geophydog/ABC

    ABC (AmBient noise and Coda) is to compute cross correlation time functions from ambient noise or coda.

    Language:C6314

  • meowoodie/Compressed-Sensing-Delay-Estimation

    Proposed to develop a low-communication cost cross-correlation method with the idea of Compressed Sensing

    Language:MATLAB6104

  • a-griffiths/AutoSpec

    Fast Automated Spectral Extraction Software for IFU Datacubes

    Language:Python5100
  • Bat-Sonar

    filipmu/Bat-Sonar

    Bat like sonar sensor that can track multiple targets and estimate angle of arrival using chirps and cross correlation in near real time.

    Language:C5102

  • mzollin/SoundLoc

    localization of sound source by cross-correlating three ΣΔ-modulated microphone signals in a zynq FPGA SoC

    Language:C++5103

  • UU-cellbiology/Correlescence

    spatial and temporal cross correlations in 1D and 2D for fluorescent microscopy (ImageJ plugin)

    Language:Java5200

  • yoavain/video-cross-correlation

    Calculate videos cross-correlation by their audio

    Language:C#5290

  • zafarrafii/uREPET-Matlab

    Matlab GUI for uREPET, a simple user interface system for recovering patterns repeating in time and frequency in mixtures of sounds.

    Language:MATLAB5001