Topic: template-matching Goto Github
Some thing interesting about template-matching
Some thing interesting about template-matching
template-matching,DRMIME - Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration
User: abnan
template-matching,A color-based classifier to detect the trees in google image data along with tree visual localization and crown size calculations via OpenCV.
User: amirniaraki
template-matching,🐢 AI for Super Auto Pets
User: andreped
template-matching,Computer Vision Essentials in Python Programming Language :tada:
User: codeperfectplus
template-matching,Rotation & scale invariant template matching
User: cozheyuanzhangde
template-matching,Information and relevant research from 2021 Auburn University REU on Smart UAVs. Computer Vision through terrain image processing, specifically through implementations of SIFT (feature matching) and template matching.
User: cvankir2
template-matching,C++ implementation of a ScienceDirect paper "An accelerating cpu-based correlation-based image alignment for real-time automatic optical inspection"
User: dennisliu1993
template-matching,The aim of this project is to use a down facing camera as a range and bearing sensor for a quadcopter for localization purposes. The environment and robot is simulated in a robot simulator V-REP, the environment consists of a 10mx10m grid with colored markers placed at regular intervals. Performance of different algorithms for marker detection is evaluated based on the error in the localization accuracy. The algorithms used are contour detection, template matching and phase correlation.
User: dheesaur
template-matching,Earthquake detection and analysis in Python.
Organization: eqcorrscan
Home Page: https://eqcorrscan.readthedocs.io/en/latest/
template-matching,Real-Time implementation of EQcorrscan methods.
Organization: eqcorrscan
Home Page: https://rt-eqcorrscan.readthedocs.io/
template-matching,Run 3 scripts to (1) Synthesize images (by putting few template images onto backgrounds), (2) Train YOLOv3, and (3) Detect objects for: one image, images, video, webcam, or ROS topic.
User: felixchenfy
template-matching,基于opencv的图像识别基础库
User: hakaboom
template-matching,图像配准算法。包括 SIFT、ORB、SURF、AKAZE、BRIEF、matchTemplate
User: hakaboom
template-matching,Pytorch Implementation of QATM:Quality-Aware Template Matching For Deep Learning
User: kamata1729
Home Page: https://arxiv.org/abs/1903.07254
template-matching,A lightweight desktop client & toolkit for writing, controlling and monitoring color-based automation scripts.
User: kelltom
template-matching,TomoBEAR is a configurable and customizable modular pipeline for streamlined processing of cryo-electron tomographic data for subtomogram averaging.
Organization: kudryashevlab
Home Page: https://github.com/KudryashevLab/TomoBEAR/wiki
template-matching,Webapplication for image stitching and aligning
User: latsic
Home Page: https://latsic.com/imgalign/info
template-matching,This project focuses on development of an algorithm for Template Matching on aerial images by implementing classical Computer Vision based techniques and deep-learning based techniques.
User: m-hamza-mughal
template-matching,Image key points Extraction, Description, Feature Matching
User: magesh-technovator
template-matching,🎓 This extension provides Moodle snippets for PHP, XML and Mustache files. It also provides commands to create new files for Moodle.
User: manuelgil
Home Page: https://marketplace.visualstudio.com/items?itemName=imgildev.vscode-moodle-snippets
template-matching,Augmented Reality Template Matching (Feature Matching) using OpenCV 4 for >= Android 5 using the NDK and an async approach (Coroutines)
User: michaeltroger
template-matching,Augmented Reality Template Matching using OpenCV 4 for >= Android 5
User: michaeltroger
template-matching,Simple Optical Character Recognizer (english-ocr-image-to-text-recognition-sample-trainig-alphabet-photo-data-database-dataset)
User: minhaskamal
Home Page: http://minhaskamal.github.io/AlphabetRecognizer
template-matching,Detects Human Skin From Image (color-region-segmentation-photo-detection-extraction-detect)
User: minhaskamal
Home Page: http://minhaskamal.github.io/SkinDetector
template-matching,Implementation of Basic Digital Image Processing Tasks in Python / OpenCV
User: mohammaduzair9
template-matching,A more object-oriented python implementation of multi-template matching (mtm), relying on scikit-image and shapely.
Organization: multi-template-matching
template-matching,Fiji plugin for object(s) detection using template(s) matching
Organization: multi-template-matching
Home Page: https://multi-template-matching.github.io/Multi-Template-Matching/
template-matching,Object-recognition using multiple templates in python
Organization: multi-template-matching
Home Page: https://multi-template-matching.github.io/Multi-Template-Matching/
template-matching,Neural Network-male-female-recognition-human-skin-detection
User: neelgajjar
template-matching,fast, high peformance image processing library for golang
User: octu0
Home Page: https://pkg.go.dev/github.com/octu0/blurry
template-matching,Windows, Unix, Raspberry Pi Computer python program to Track Camera X, Y Movements and Convert to Camera Pointing Position. Useful for Stabilization or Robotics Course Correction
User: pageauc
template-matching,A simple python bot (powered by computer vision) used to play a game (City Island 5). The bot is able to play the game and collect points without any human intervention.
User: paulonteri
Home Page: https://paulonteri.com/thoughts/play-game-with-computer-vision
template-matching,PyTrx is a Python object-oriented programme created for the purpose of calculating real-world measurements from oblique images and time-lapse image series. Its primary purpose is to obtain velocities, surface areas, and distances from oblique, optical imagery of glacial environments.
User: pennyhow
template-matching,This is an example to find multiple objects in images that match a template using ORB and SIFT feature detection methods. Handles different scales and rotations.
User: prob1995
template-matching,A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)
User: rmislam
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.
User: robertarvind
Home Page: https://github.com/robertarvind
template-matching,Detection algorithms and applications from famous papers; simple theory; solid code.
User: saimj7
template-matching,Kernel Cross-Correlator (KCC) for Tracking and Recognition (AAAI 2018)
Organization: sair-lab
template-matching,Functions for automating osrs botting using Python.
User: slyautomation
template-matching,Fast and scalable spike sorting in python
Organization: spyking-circus
Home Page: http://spyking-circus.rtfd.org
template-matching,Automated Testing for Set-Top Boxes and Smart TVs
Organization: stb-tester
Home Page: https://stb-tester.com
template-matching,Topographic edge detection of fault scarps and other landforms in digital elevation data
Organization: stgl
Home Page: https://scarplet.readthedocs.io
template-matching,Test for template matching using node-opencv
User: strarsis
template-matching,A computationally efficient earthquake detection module for SeisComP
Organization: swiss-seismological-service
Home Page: https://scdetect.readthedocs.io
template-matching,This project aims to find the shortest path for cross stitching for each colour in a patten.
User: talitahalboth
template-matching,A repo designed to convert audio-based "weak" labels to "strong" intraclip labels. Provides a pipeline to compare automated moment-to-moment labels to human labels. Methods range from DSP based foreground-background separation, cross-correlation based template matching, as well as bird presence sound event detection deep learning models!
Organization: ucsd-e4e
template-matching,GPU-accelerated template matching for Rust
User: urholaukkarinen
template-matching,:trident: Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.
User: yeaseen
template-matching,Matched filter earthquake detector
User: yijianzhou
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