Claudio S. De Mutiis's repositories
Advanced_Lanes_Detection
Advanced Lanes Detection using camera calibration, gradient/colour thresholding, a bird's-eye view perspective transform, a sliding window search and quadratic polynomial fits
Vehicle_Detection_and_Tracking
Use a Histogram of Oriented Gradients (HOG), Spatial Binning of Color, Histograms of Color, a Linear Support Vector Machine and multi-scale sliding windows for vehicle detection and tracking
highway_lanes_detection
Detecting highway lane lines on a video stream with a Hough Transform and a Canny edge detector
Traffic_Sign_Classifier
Traffic Sign Classifier using a convolutional neural network
EM_LEARNING
EM learning for a mixture of K multivariate Bernoullis with binary images
LSTM_RNN_Text_generation
Using the book Anna Karenina by Leo Tolstoy as a training source for text generation using RNN and LSTM
Model_Predictive_Control
Using Model Predictive Control to drive a car around the track on the Udacity Simulator
Behavioral-Cloning
Teach a convolutional neural network (NVIDIA architecture) how to drive using the Udacity self-driving car simulator
Boston_housing_prices
Predicting Boston Housing Prices with Decision Trees
CNN_CIFAR10
Use a convolutional neural network to classify the dataset CIFAR 10
Extended_Kalman_Filter
Using a Kalman Filter to estimate the state of a moving object of interest with noisy Lidar and Radar measurements
keras-preprocessing
Utilities for working with image data, text data, and sequence data.
Kidnapped_Vehicle
Using a 2-dimensional Particle Filter to localize a vehicle
linear_gp_regression
Bayesian linear and Gaussian process regression to predict CO2 concentration as a function of time
Local-Odometry-Techniques-MIRTO-
- Provided the MIRTO robot, designed and built by a team led by Dr. Franco Raimondi (F.Raimondi@mdx.ac.uk) at Middlesex University London, with autonomous navigation planning capabilities. - Wrote a library of high-level odometrical functionalities (i.e. Java methods) to allow MIRTO to perform actions such as rotating, translating and moving towards a specific point in space while avoiding all obstacles on the way. - Developed a navigation algorithm that only makes use of MIRTO's wheels' encoders and bumpers sensors. - Used MIRTO to test the newly developed navigation algorithm.
NLP_regression
First stage of a biomedical event extractor
PID_controller
Using a PID controller to drive a car around the track on the Udacity Simulator
self-driving-car
The Udacity open source self-driving car project
tensorflow
Computation using data flow graphs for scalable machine learning
transfer_learning
Transfer Learning
Unscented_Kalman_Filter
Using an Unscented Kalman Filter to estimate the state of a moving object of interest with noisy lidar and radar measurements
deep-reinforcement-learning
Repo for the Deep Reinforcement Learning Nanodegree program