IacopomC/CarND-Advanced-Lane-Lines

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Created over 4 years ago
Updated over 4 years ago

IacopomC/CarND-Advanced-Lane-Lines

IacopomC

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Image processing pipeline for detecting highway lane in a video

Sensor-Fusion-3D-Multi-Object-Tracking

Extended Kalman Filter and Deep Learning to detect vehicles from RGB and LiDAR data (Sensor Fusion and Tracking project of the Udacity Self-Driving Car Engineer Nanodegree Program)

robot_arm_three_js

Robot arm with 3 degrees of freedom built using three.js

Equirectangular-Cubemaps-Transform-CUDA-OpenMp

Conversion of 360 stereo images from equirectangular to cube maps using CUDA and OpenMp

Threejs-practice

Practice exercises using three.js

LUT_interpolation

LUT trilinear interpolation

openMp-CUDA-Image-Processing

Image Processing Methods implemented with openMP, CUDA and shared memory

CarND-Behavioral-Cloning-P3

CNN model to drive autonomously a car in a simulator using Keras

Jupyter Notebook

3D-Object-Tracking

CarND-LaneLines-P1

Basic computer vision pipeline to detect lane lines in a video.

Jupyter Notebook

Shader-Image-Processing

A series of image processing algorithms using Three JS and shaders

Color-Segmentation-CUDA

Color segmentation via Lab color space using CUDA

Color_Science_Lab

Color Science Lab built using only three.js

CarND-Vehicle-Detection

Vehicle Detection and Tracking pipeline with OpenCV, HOG and SVM.

Jupyter Notebook

ET_Project

Eye tracking course final project.

Jupyter Notebook

2D-Feature-Extraction

CarND-Traffic-Sign-Classifier-Project

CNN and Data Augmentation to train a traffic sign classifier with OpenCv and Tensorflow

Jupyter Notebook

d3.js-visualization

Practice exercises using d3.js

iacopomc.github.io