Cursusaanbod

Introduction to Computer Vision for Robotics

  • Overview of computer vision applications in robotics
  • Key challenges in perception and visual understanding
  • Setting up the development environment with OpenCV and Python

Image Processing Fundamentals

  • Image representation and manipulation
  • Filtering, edge detection, and feature extraction
  • Color spaces and segmentation techniques

Object Detection and Tracking with OpenCV

  • Detecting objects using classical methods (Haar cascades, HOG)
  • Tracking moving objects in video streams
  • Integrating visual feedback into robotic systems

Deep Learning for Visual Perception

  • Overview of convolutional neural networks (CNNs)
  • Training and deploying object detection models
  • Applying pre-trained models (YOLO, SSD, Faster R-CNN)

Sensor Fusion and Depth Perception

  • Integrating camera data with LiDAR and ultrasonic sensors
  • Depth estimation and 3D reconstruction
  • Perception for obstacle avoidance and navigation

Vision-Based Control and Decision Making

  • Applying computer vision to robotic manipulation
  • Visual servoing and closed-loop control
  • Autonomous decision-making based on visual input

Deploying and Optimizing Vision Models

  • Deploying models on embedded systems and edge devices
  • Optimizing inference performance for real-time applications
  • Troubleshooting and improving accuracy

Summary and Next Steps

Vereisten

  • An understanding of basic robotics concepts
  • Experience with Python programming
  • Familiarity with machine learning fundamentals

Audience

  • Robotics engineers
  • Computer vision practitioners
  • Machine learning engineers
 21 Uren

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