Basic numpy implementation of Otsu and Niblack algoritms
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Updated
May 18, 2022 - Jupyter Notebook
Basic numpy implementation of Otsu and Niblack algoritms
OTSU method is a global adaptive binarization threshold image segmentation algorithm.
Through the use of Contrast Limited Adaptive Histogram Equalization (CLAHE) filters, completed with otsu filters, a direct reading of car license plates with success rates above 70% and an acceptable time is achieved
Implementation of a Shape Detection pipeline for recognizing those famous traffic bollards found in Amsterdam without any Image Processing libraries
From some files of images and labels obtained by applying the project presented at https://github.com/ashok426/Vehicle-number-plate-recognition-YOLOv5, the images of license plates are filtered through a threshold that allows a better recognition of the license plate numbers by pytesseract. On 05/23/2022, a new version is introduced. On 07/04/20…
Counting cells in a blood smear using convolution as the pattern matching strategy
In water index calculation, gets the best threshold and full area of water according to known parts or approximation of water.
Prewitt edge detector: gradient filter és nonmaxima-suppression (NMS), Thresholding algorithm by Otsu, Detection of circular object by edge detection and Hough transform for circles, Motion tracking of feature points and dense optical flow
Using together cv2's findcontours and Haarcascade license plate detection together with the application of an extensive set of filters
Character recognition on the street sign in Indonesian Cities
Java implementation of the otsu algorithm for image binarization
Files accompanying the paper "Segmentation of Blood Cell Images Using Evolutionary Methods", published in 2013.
Proyecto que consiste en segmentar y reconocer los caracteres de una matricula de coche
Using Otsu's thresholding for text segmentation on images of sticky notes.
From images of cars in which their license plates have been labeled, and passing filters, their recognition is attempted by pytesseract . As there is not a single filter that works for all the licensess, it is tried with several filters and The license plate number that has been detected the most times is assigned.
Proyecto de prácticas de la asignatura Inteligencia Ambiental.
This projects reflects the 3D reconstruction of a protein aggregate, after a careful processing (filtering, segmentation, reconstruction) of a set of slices of a protein aggregate.
A recognition licenses plates based in FindContours
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