cross-correlation
There are 1 repository under cross-correlation topic.
- Language:Python 324
- PIVlab
Shrediquette / PIVlab
Particle Image Velocimetry for Matlab, official repository
Language:MATLAB 185 eqcorrscan / EQcorrscan
Earthquake detection and analysis in Python.
Language:Python 179alopatindev / sync-audio-tracks
Audio tracks synchronization command-line tool for video editors that don't support it
Language:C++ 72- SeisNoise.jl
JuliaSeismo / SeisNoise.jl
Ambient Noise Cross-Correlation in Julia
Language:Julia 59 - Language:Python 43
YijianZhou / MESS
Matched filter earthquake detector
Language:Python 43chaddy1004 / sobel-operator-pytorch
Sobel edge detection implemented on PyTorch
Language:Python 40H-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:MATLAB 30- Language:MATLAB 28
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:Python 24OpenSMFS / pycorrelate
Fast and accurate cross-correlation over arbitrary time lags.
Language:Python 23freemocap / skelly_synchronize
Synchronization tool for videos of the same event. Uses audio cross correlation to synchronize.
Language:Python 20GKalliatakis / Fourier-Analysis-Digital-Images
Fourier analysis applications for image matching.
Language:MATLAB 19swiss-seismological-service / scdetect
A computationally efficient earthquake detection module for SeisComP
Language:C++ 18SaiferGit / 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:MATLAB 17vidify / old-audiosync
First implementation of the audio synchronization feature for Vidify, now obsolete
Language:C 17- Language:C 16
- Language:Python 10
asuszko / signal_cross_correlation
Custom CUDA kernel doing a normalized cross correlation on a batch of signals via pycu_interface.
Language:Python 10imodpasteur / 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:Python 10zlogic / cybervision
Cybervision can generate a 3D model from two photos of an object
Language:Rust 10- Language:C 9
uttamsaha / image-processing
This is 3*3 mean Filter, min filter, max filter, weighted average filter and 5*5 mean filter, min filter and max filter using JavaScript Program of image processing problem solving.
Language:JavaScript 9- Language:C++ 8
UU-cellbiology / Correlescence
spatial and temporal cross correlations in 1D and 2D for fluorescent microscopy (ImageJ plugin)
Language:Java 8alexscarlatos / filmio
A utility to find the best audio matches for videos and sync them together, perhaps for your films
Language:Python 7- Language:Julia 7
mzollin / SoundLoc
localization of sound source by cross-correlating three ΣΔ-modulated microphone signals in a zynq FPGA SoC
Language:C++ 7ErichZimmer / Fluidity
A Python GUI for Digital Particle Image Velocity (DPIV)
Language:Python 6fwolling / PulSync
PerHealth'21 - PulSync: The Heart Rate Variability as a Unique Fingerprint for the Alignment of Sensor Data Across Multiple Wearable Devices
Language:Jupyter Notebook 6geophydog / ABC
ABC (AmBient noise and Coda) is to compute cross correlation time functions from ambient noise or coda.
Language:C 6meowoodie / Compressed-Sensing-Delay-Estimation
Proposed to develop a low-communication cost cross-correlation method with the idea of Compressed Sensing
Language:MATLAB 6ocramz / vector-fft
FFT and IFFT for `vector`, with minimal dependencies
Language:Haskell 6SimonVaughanDataAndCode / sour
Cross correlation tools for R
Language:R 6yoavain / video-cross-correlation
Calculate videos cross-correlation by their audio
Language:C# 6
