What is the first step in preparing an image for feature detection in this lecture?
Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] FAST/ORB Featu

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Performing edge detection
Loading the image
Creating a detector object
Converting the image to grayscale
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main advantage of using Canny edge detection over Sobel or Laplacian methods?
It requires fewer parameters
It is faster
It is less noisy
It is more colorful
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of creating a FAST detector object?
To convert the image to grayscale
To detect key points in the image
To perform edge detection
To resize the image
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of key points in feature detection?
They remove noise from the image
They change the color of the image
They are used to resize the image
They highlight the most important pixels
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What additional information does ORB feature detection provide besides key points?
Color histograms
Descriptors
Edge maps
Noise levels
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is ORB feature detection not used extensively in this lecture?
It requires more computational power
It is not supported by OpenCV
Canny edge detection is preferred for training the classifier
It is too complex
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the final step in the lecture after detecting features?
Drawing the key points on the image
Converting the image to grayscale
Performing edge detection
Resizing the image
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