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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
10.
Deep Learning Integration
10.1.
DNN Module Overview
10.1.1.
Supported Frameworks
10.1.1.1.
Caffe
10.1.1.2.
TensorFlow
10.1.1.3.
PyTorch
10.1.1.4.
Darknet
10.1.1.5.
ONNX
10.1.2.
Model Loading Process
10.1.3.
Inference Pipeline
10.2.
Image Classification
10.2.1.
Pre-trained Models
10.2.2.
Input Preprocessing
10.2.2.1.
Image Resizing
10.2.2.2.
Normalization
10.2.2.3.
Mean Subtraction
10.2.3.
Forward Pass Execution
10.2.4.
Output Interpretation
10.2.5.
Top-K Predictions
10.3.
Object Detection Networks
10.3.1.
Single Shot MultiBox Detector
10.3.1.1.
SSD Architecture
10.3.1.2.
Anchor Box Concepts
10.3.1.3.
Multi-scale Detection
10.3.2.
YOLO Family
10.3.2.1.
YOLOv3 Implementation
10.3.2.2.
YOLOv4 Implementation
10.3.2.3.
YOLOv5 Integration
10.3.3.
Detection Pipeline
10.3.3.1.
Blob Creation
10.3.3.2.
Network Inference
10.3.3.3.
Post-processing
10.3.3.4.
Non-Maximum Suppression
10.4.
Semantic Segmentation
10.4.1.
Segmentation Models
10.4.2.
Pixel-wise Classification
10.4.3.
Mask Generation
10.4.4.
Visualization Techniques
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9. Camera Calibration and 3D Vision
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1. Introduction to OpenCV