Computer Vision

Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. [...]

Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of [...]

By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify [...]

Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law enforcement agencies. In this beginner-friendly course you will understand about computer vision, and will learn about its various [...]

This course is designed for application developers who wants to deploy computer vision inference workloads using the Intel® Distribution of OpenVINOTM toolkit. The course looks at computer vision neural network models from a variety of popular machine learning frameworks and covers writing a portable application capable of deploying inference [...]

How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks? In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow. Such 2D representations allow us then [...]