What You Need to Know Before
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Starts 8 June 2025 12:01
Ends 8 June 2025
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37 minutes
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Overview
Explore the essential principles and concepts of computer vision in this introductory segment of a broader computer vision series.
Syllabus
- **Introduction to Computer Vision**
- **Digital Image Fundamentals**
- **Image Processing Techniques**
- **Feature Detection and Matching**
- **Basic Image Segmentation**
- **Geometric Transformations**
- **Introduction to Machine Learning in Vision**
- **Project and Case Studies**
- **Course Summary and Next Steps**
Overview of Computer Vision
History and Applications
Key Challenges
Image Representation and Types
Color Spaces and Conversions
Basic Operations on Images
Image Enhancement
Thresholding and Binary Images
Filtering (Smoothing and Sharpening)
Edges and Contours
Keypoint Detectors (SIFT, SURF, ORB)
Feature Matching Techniques
Region-Based Segmentation
Clustering Methods (K-means)
Watershed Algorithm
Image Translation, Rotation, and Scaling
Affine and Perspective Transformations
Homography
Basics of Supervised and Unsupervised Learning
Introduction to Convolutional Neural Networks
Simple Object Recognition Example
Practical Project involving Basic Vision Tasks
Analysis of Real-world Computer Vision Applications
Review of Key Concepts
Introduction to Advanced Topics in Part 2
Further Reading and Resources
Subjects
Computer Science
