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

The video tutorial demonstrates how to apply Histogram of Oriented Gradients (HOG) to extract features from an image of a truck using Python and OpenCV. It covers loading libraries and images, converting images to grayscale, applying Sobel filters, and implementing HOG to extract and analyze features. The tutorial also explains rescaling intensity for better visualization and discusses using these features in machine learning classifiers to detect objects like cars and trucks.

What is the primary purpose of loading libraries and images in the initial setup?

• To apply machine learning algorithms

• To prepare for HOG feature extraction

• To enhance image colors

• To convert images to 3D models

Why is the image converted to grayscale before applying the Sobel operator?

• To apply color filters

• To increase image size

• To enhance color features

• To reduce computational complexity

What does the Sobel operator help to calculate in an image?

• Color intensity

• Image brightness

• Image gradients

• Image contrast

Which parameter specifies the number of angles in HOG feature extraction?

• Pixels per cell

• Cells per block

• Image resolution

• Orientations

What is the final step in visualizing HOG features?

• Applying color filters

• Rescaling intensity

• Reducing image noise

• Increasing image size