Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Code to define

The video tutorial covers the concept of region of interest (ROI) in image processing using Python. It begins with an introduction to ROI, followed by converting a colored image to grayscale. The tutorial then explains how to manually define points for the ROI and create a mask. Finally, it demonstrates applying the mask to the image using bitwise operations, allowing for focused image analysis.

What is the primary objective when focusing on the region of interest in self-driving cars?

• Identifying road signs

• Detecting lane lines

• Monitoring pedestrian movement

• Measuring vehicle speed

Why is it important to convert an image to grayscale before processing?

• To simplify the image data

• To increase processing speed

• To enhance color details

• To reduce the image size

What is the purpose of manually identifying region of interest points?

• To automate the image processing

• To adjust the image brightness

• To enhance image resolution

• To define specific areas for analysis

What is the first step in creating a mask for the region of interest?

• Converting the image to binary format

• Filling the mask with white pixels

• Applying a filter to the image

• Creating a black image of the same size

How is the region of interest applied to the image after creating the mask?

• By performing a bitwise AND operation

• By using a thresholding technique

• By performing a bitwise OR operation

• By using a convolution operation

What happens if you change the Y direction of the region of interest points?

• The height of the region changes

• The color of the region changes

• The region becomes inverted

• The width of the region changes

What is the effect of adjusting the points closer in the region of interest?

• It increases the area of interest

• It decreases the area of interest

• It changes the image resolution

• It alters the image contrast