Computer Vision and Classification

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01. Deep Learning at NVIDIA
02. Classifying Sebastian
03. Welcome to Computer Vision
04. Introducing Tarin
05. Vision and Self-Driving Cars
06. LiDAR Data
07. Image Classification Pipeline
08. Quiz: Classification Steps
09. Learning to Classify Images
10. What is Machine Learning?
11. Training a Model
12. Quiz: Choose Layers of Separation
13. Images as Grids of Pixels
14. Notebook: Images as Numerical Data
15. Color Images
16. Color or Grayscale?
17. Notebook: Visualizing RGB Channels
18. Pre-processing
19. Notebook: Cropping and Resizing
20. Color Masking
21. Installing OpenCV, Instructions
22. Green Screen Car
23. Notebook: Green Screen Background
24. Color Spaces and Transforms
25. HSV Conversion
26. Notebook: Color Conversion
27. Day and Night Classification
28. Notebook: Load and Visualize the Data
29. Labeled Data and Accuracy
30. Distinguishing Traits
31. Feature Extraction
32. Features
33. Standardizing Output
34. Notebook: Standardizing Day and Night Images
35. Average Brightness
36. Notebook: Average Brightness Feature Extraction
37. Features and Classification
38. Selecting Features
39. Filters and Finding Edges
40. High-pass Filter
41. Quiz: Kernels
42. Notebook: Finding Edges
43. Convolution in Self-Driving Cars
44. Notebook: Histograms and Feature Vectors
45. Classification
46. Notebook: Classification
47. Convolutional Neural Networks
48. Evaluation Metrics
49. Notebook: Accuracy and Misclassification
50. Congratulations!!
51. Ends and Beginnings!

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41. Quiz: Kernels

Kernel Convolution

Now that you know the basics of high-pass filters, let's see if you can choose the best one for a given task.

Of the four kernels pictured above, which would be best for finding and enhancing horizontal edges and lines in an image?

a

b

c

d

SOLUTION:

d

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