jvirico/sift-feature-matching

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Python
Created about 3 years ago
Updated about 3 years ago

jvirico/sift-feature-matching

jvirico

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SIFT feature matching using OpenCV.

kalman-tracker

An algorithm for object tracking based on Kalman Filter is implemented using OpenCV C++ library. Two modes of operation are coded, a Constant Velocity Model, and an Acceleration Model.

mouse_CT_3D_reconstruction

3D reconstruction of a mouse from a collection of Sinograms coming from a Computed Tomography scan, with Filtered Backprojection and Matlab.

slam-navigation

SLAM navigation on simplified scenario (FastSLAM implementation using Python) based on Particle Filter (Sequential Monte Carlo). What happens when the visual support of a drone is missing?

Jupyter Notebook

normality-tests-pvalues-boxcoxtransformations

Strategies for analyzing the distribution of datasets, switching the data towards a normal distribution testing different manual transformations and Box-Cox transformation.

scania-truck-failure-prediction

Data Mining and Machine Learning APS Failure at Scania Trucks Data Set.

Jupyter Notebook

point-cloud-manipulation

Voxels manipulation and mesh generation using Python.

gans-keras

Multilayer Perceptron GAN, and two Convolutional Neural Network GANs for MNIST and CIFAR.

binary_expression_tree

Java implementation of a Binary Expression Tree to manage algebraic expressions using Composite and Visitor design patterns.

pytorch-templates

Code templates for common Neural Networks in PyTorch, including Perceptron, MLP, Radial Basis Function Nets, GANs...

augmented-reality-poc-unity

AR app built on Android’s ARCore that allows to virtually place multiple true-to-scale 3D object models in a real environment using a mobile phone. By simply uploading new objects to the cloud-based catalog of 3D objects, it can be easily extensible to other use cases for fast creation of virtual prototypes on top of existing real spaces.

composite-pattern

Small examples of Composite design pattern implemented in Java and Python.

graph-conv-neural-nets

ACGAN_Chromos

Adaptation of ACGAN to fit Chromosome images for Data Augmentation and Chromosome type conditioning.

object_detection_classification

Object detection and classification using Computer Vision, OpenCV, and C++. Implements: (1) blob extraction using SequentialGrass-Fire algorithm, removing the blobs with a size below a certain threshold to eliminate noise; (2) Blob classification using Aspect Ratio feature and simple statistical classifier; (3) Implementation of extraction of stationary foreground pixels based on foreground history; (4) Custom implementation of Grass-Fire algorithm without using OpenCV’s in-built Connected Component Analysis functionalities. (5) Attempt to improve blob classification by using statistical properties of color channels. The implementation is tested on two datasets.