Repository with Machine Vision Projects
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Updated
May 28, 2024 - Jupyter Notebook
Repository with Machine Vision Projects
Modified CoALTP Descriptor: An enhanced feature extraction method for image analysis, improving accuracy and robustness.
Feature extraction of surface defect images based on Grey-Level Co-occurrence Matrix(GLCM) and classification using multi-layer perceptron and k-nearest neighbor classifier
A prototype of a CBIR food-retrieval application using GLCM and CIE Lab color space.
This project is used to compare two model that is being made using Support Vector Machine (SVM) algoritm that is being trained by using one dataset, but the model is trained using a dataset that is being preprocessed using some preprocessing techniques, while the other one is just the plain image dataset.
Another technique of implementing GLCM algorithm
Recognition of different material using its texture feature out of the video file
This repository contains a pipeline for creating annotations for images and generating a dataset from these annotations. It also includes unit tests for the models used within the pipeline. The pipeline incorporates several machine learning models that are trained and tested on a dataset.
This is a thesis that I did to get a Bachelor's degree in Informatics at MDP University. On this repository you can use it for classification using the SVM method, SVM-GLCM, SVM-Color Moments, and SVM-GLCM-Color Moments by using several kernels such as linear, polynomial, and RBF by replacing the kernel. In addition there is a script that can be…
This repository includes my work in the journey to learn Image Processing.
This is an image processing mini project that provides base required values of particular image to user by calculating from converted grayscale image.
Satelite image classification in South Zone, Rio de Janeiro
Code written for MATH 444 projects Spring 2021
These are the scripts I used at my summer school at IIT BHU for image processing and anomaly detection.
Foreco project which include efforts to define and analyse relationships between Forest structure and forest disturbance like Bark Beetle, storm and drought dynamics.
Código empleado para la clasificación de imágenes de resonancia magnética para la detección de la enfermedad de Alzheimer a partir de la técnica GLCM y los algoritmos de clasificación: KNN, random forest, árbol de decisión y regresión logística.
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