Computer Vision and Image Processing

Computer Vision and Image Processing

$379.00

Course Overview

Enter the exciting world of computer vision and learn how machines perceive and understand visual information. This advanced course covers cutting-edge techniques in image processing, object detection, and real-time video analysis.

Build powerful computer vision applications using state-of-the-art deep learning models. From facial recognition to autonomous vehicle vision systems, you'll master the skills needed to create AI systems that can see and interpret the world.

What You'll Learn

  • Image processing fundamentals and techniques
  • Convolutional Neural Networks for vision tasks
  • Object detection and instance segmentation
  • Facial recognition and biometric systems
  • Image classification and feature extraction
  • Real-time video processing and analysis
  • 3D vision and depth estimation
  • Advanced architectures: YOLO, R-CNN, Mask R-CNN

Course Curriculum

Module 1: Computer Vision Fundamentals

Image representation, color spaces, and basic image operations

Module 2: Image Processing Techniques

Filtering, edge detection, morphological operations, and transformations

Module 3: Feature Detection and Matching

Corner detection, SIFT, SURF, and feature descriptors

Module 4: Deep Learning for Vision

CNNs, popular architectures, and transfer learning for vision tasks

Module 5: Object Detection

R-CNN family, YOLO, SSD, and real-time detection systems

Module 6: Image Segmentation

Semantic segmentation, instance segmentation, and Mask R-CNN

Module 7: Facial Recognition

Face detection, recognition, and verification systems

Module 8: Advanced Topics

Video analysis, 3D vision, optical flow, and tracking

Meet Your Instructor

Dr. James Patterson

Computer Vision Specialist & Robotics Expert

Dr. James Patterson is a renowned computer vision researcher with a PhD from MIT. He has developed vision systems for autonomous vehicles and has contributed to numerous open-source computer vision projects.

With 15 years of experience in computer vision and robotics, he has worked with leading technology companies on groundbreaking visual AI applications. His teaching combines rigorous theory with hands-on practical implementation.

Course Requirements

Prerequisites:

  • Strong Python programming skills
  • Understanding of deep learning concepts
  • Linear algebra and calculus knowledge
  • Experience with NumPy and basic image manipulation

What's Included:

  • 45 hours of detailed video content
  • Computer vision code repositories
  • Real-world project implementations
  • Professional certificate
  • Access to vision datasets