Title: Robotics. Lesson 5; Robotic Perception and Object Recognition

Title: Robotics. Lesson 5; Robotic Perception and Object Recognition

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1. Convolutional neural networks (CNNs) process images for object detection tasks.

2. Semantic segmentation classifies each pixel in an image by category.

3. Feature extraction identifies key patterns in visual data for recognition.

4. Edge detection algorithms highlight object boundaries for improved segmentation.

5. Depth estimation calculates distance from the robot to detected objects.

6. YOLO (You Only Look Once) enables real-time object detection in robotics.

7. Object tracking algorithms monitor movement and position of target objects.

8. Stereo vision uses two cameras for 3D perception in robotics.

9. LiDAR generates 3D maps, aiding in object localization and navigation.

10. Image preprocessing enhances data quality, improving recognition accuracy.

11. SIFT (Scale-Invariant Feature Transform) identifies unique features in images.

12. Optical flow estimates motion by tracking pixel movement between frames.

13. Haar cascades detect specific objects based on pre-trained patterns.

14. Object classification assigns detected objects to predefined categories.

15. Sensor fusion combines visual and non-visual data for perception.

16. SLAM (Simultaneous Localization and Mapping) maps surroundings while locating objects.

17. Background subtraction isolates moving objects from static environments.

18. Gaussian Mixture Models identify foreground objects in video feeds.

19. Point cloud processing extracts features from 3D data in robotics.

20. Faster R-CNN achieves high accuracy in complex object recognition tasks.

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Technical Examples

1. Depth Estimation Example: Calculating object distances using stereo vision for robotic navigation.

2. Object Tracking Example: Following a moving object with real-time position updates.

3. Feature Extraction Example: Using SIFT to identify unique object features in a scene.