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Computer Science
Computer Vision
Computer Vision with OpenCV
1. Introduction to OpenCV
2. Core Operations and Data Structures
3. Image Processing Fundamentals
4. Histograms and Image Analysis
5. Feature Detection and Description
6. Object Detection and Recognition
7. Video Analysis and Object Tracking
8. Advanced Image Processing
9. Camera Calibration and 3D Vision
10. Deep Learning Integration
Object Detection and Recognition
Template Matching
Matching Methods
Squared Difference
Correlation Coefficient
Normalized Methods
Multi-scale Template Matching
Result Interpretation
Hough Transforms
Hough Line Transform
Standard Hough Transform
Probabilistic Hough Transform
Parameter Space Mapping
Hough Circle Transform
Accumulator Array
Parameter Tuning
Multiple Circle Detection
Contour Analysis
Contour Detection
Retrieval Modes
Approximation Methods
Hierarchy Information
Contour Drawing and Visualization
Contour Properties
Moments Calculation
Area Computation
Perimeter Calculation
Centroid Location
Bounding Shapes
Bounding Rectangle
Minimum Enclosing Circle
Ellipse Fitting
Convex Hull
Shape Analysis
Aspect Ratio
Extent
Solidity
Equivalent Diameter
Cascade Classifiers
Haar Cascade Principles
Integral Images
Haar-like Features
AdaBoost Training
Cascade Structure
Using Pre-trained Classifiers
Face Detection Models
Eye Detection Models
Full Body Detection Models
Detection Process
detectMultiScale Function
Scale Factor Parameter
Minimum Neighbors Parameter
Size Constraints
Custom Classifier Training
Positive Sample Collection
Negative Sample Generation
Training Pipeline
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7. Video Analysis and Object Tracking