An introductory course on machine vision and related machine learning used in automation, autopilots, security and inspection systems. Topics covered include theory of computer and machine vision and related algorithms for image capture and processing, filtering, thresholds, edge detection, shape analysis, shape detection, salient object detection, pattern matching, digital image stabilization, stereo ranging, and methods of sensor and information fusion. Machine vision sensors covered include visible to long-wave infrared including passive EO/IR (Electro-Optical/Infrared) as well as active methods such as RGB depth mapping and LIDAR. Embedded and automation topics covered include implementation of these algorithms with FPGA or GP-GPU embedded real-time vision systems for autopilots (intelligent transportation), general machine vision automation and security including methods for detection, classification, recognition of targets, and applications including inspection, surveillance, search and rescue, and machine vision navigation.

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Contains all excersize and final project code for Embedded Machine Vision and Intelligent Automation, CU Boulder, 2021

Consists of Projects and Assignments Completed in Embedded Machine Vision and Intelligent Automation Course | ECEN 5763 | University of Colorado, Boulder.

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This is Repository for my Course Project on Self Driving Car

Embedded Machine Vision and Intelligent Automation

Contains Assignments of Embedded Machine Vision and Intelligent Automation Summer 2022

Final project as a part of Embedded Machine Vision and Intelligent Automation coursework.

This repository contains the work developed for the Final Project of ECEN 5763 - Embedded Machine Vision and Intelligent Automation Course under Dr. Sam Siewert

Codes and file for ECEN 5863, Embedded Machine Vision & Intelligent Automation. Final project implements a driving assist program with custom scheduler and resource management between threads.