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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?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.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 Androidโs 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
sift-feature-matching
SIFT feature matching using OpenCV.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 OpenCVโs 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.Love Open Source and this site? Check out how you can help us