Certificate in Visual AI for Autonomous Vehicles
-- ViewingNowThe Certificate in Visual AI for Autonomous Vehicles is a comprehensive course that focuses on the critical role of visual AI in self-driving vehicles. This course highlights the importance of computer vision, deep learning, and sensor fusion in enabling autonomous vehicles to perceive, understand, and navigate their surroundings.
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โข Introduction to Visual AI & Computer Vision: Understanding the basics of Visual AI, computer vision, and their applications in autonomous vehicles.
โข Image and Video Processing: Learning about image and video processing techniques, filters, and transformations.
โข Object Detection and Recognition: Identifying and classifying objects in images and videos, including vehicles, pedestrians, and traffic signs.
โข Semantic Segmentation and Instance Segmentation: Segmenting images into meaningful regions and identifying individual instances of objects.
โข Deep Learning for Visual AI: Exploring deep learning techniques and architectures for Visual AI, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
โข 3D Perception and Reconstruction: Understanding 3D perception and reconstruction techniques, such as Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM).
โข Visual AI for Autonomous Navigation: Applying Visual AI for autonomous navigation, including path planning, obstacle avoidance, and lane detection.
โข Ethical and Safety Considerations: Examining ethical and safety considerations related to using Visual AI in autonomous vehicles, including privacy, security, and liability.
โข Real-World Applications and Challenges: Exploring real-world applications and challenges of Visual AI in autonomous vehicles, including weather and lighting conditions, and sensor fusion.
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