My solution to the Udacity Self-Driving Car Engineer Nanodegree Vehicle Detection and Tracking project.
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Updated
May 10, 2017 - Jupyter Notebook
My solution to the Udacity Self-Driving Car Engineer Nanodegree Vehicle Detection and Tracking project.
Autonomous Car Navigation & Control Through Computer Vision and Sensor Fusion
Created a basic Facial recognition system by performing image processing on bunch of images using Opencv and haar-cascades. system can perform still image recognition as well as real-time processing is possible.
💻 🧐 NTUA ECE Computer Vision Source Codes
Biometric authentication by the user's face using a single camera (on a PC or laptop).
A project for real time detection and counting of number of human in a photo and video using HOG and SVM.
Project 5 of Udacity Self Driving Car Nanodegree
Train a classification model to identify the product category, utilizing either classical computer vision or deep learning methods. Utilize a one/few-shot learning model to confirm the existence of a product and accurately classify its type.
Udacity Self-Driving Car Engineer Nanodegree Vehicle Detection and Tracking project.
Logic Lens is an application that allows you to get the truth table of a logical expression, or the corresponding logical expression of a given truth table, only by taking a picture of it!
applying SIFT and HOG to localize cropped pieces of text
Udacity CarND - Vehicle Detection and Tracking
Detect Vehicles in a video.Train a SVM classifier to detect vehicles and using it to detect vehicles in the video.Competed as part of Udacity Self Driving Car NanoDegrel
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