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

This lecture covers the process of Harris corner detection, starting with library imports and testing on a chessboard image. The instructor explains image conversion to grayscale, corner detection, and enhancing corner visibility through dilation. The lecture also demonstrates applying color to detected corners and extends the technique to a truck image. The session concludes with a recap of the steps and outcomes.

What is the primary purpose of using a chessboard image in Harris Corner Detection?

• To reduce the image size

• To enhance the color contrast

• To apply a filter effect

• To test the algorithm on a known pattern

Which step is crucial before applying Harris Corner Detection on an image?

• Increasing the image brightness

• Resizing the image

• Applying a Gaussian blur

• Converting the image to grayscale

What is the purpose of using dilation in corner detection?

• To apply a sharpening effect

• To enhance the visibility of detected corners

• To change the color of the image

• To reduce the number of detected corners

How does setting a threshold help in corner detection?

• It applies a color filter

• It changes the image format

• It increases the image resolution

• It identifies pixels that are likely corners

What color is used to highlight detected corners in the tutorial?

• Red

• Blue

• Yellow

• Green

What challenge is faced when applying Harris Corner Detection on a truck image?

• The image is too small

• The image is too bright

• The image is not uniform

• The image is in black and white

What is the effect of adjusting the threshold in corner detection?

• It improves the robustness of the algorithm

• It changes the image color

• It reduces the image size

• It applies a blur effect