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

This lecture covers the process of extracting color features from images using histograms. It begins with downloading and displaying an image, followed by plotting histograms for the red, green, and blue color channels using OpenCV. The lecture also discusses potential applications, such as lane detection, and encourages further exploration with different images. The session concludes with a recap of the steps taken to plot color histograms.

What is the primary focus of this lecture?

• Exploring audio signal processing

• Learning about image compression

• Extracting color features using histograms

• Understanding video processing

Which library is used to plot histograms in this lecture?

• OpenCV

• NumPy

• TensorFlow

• Pandas

What is the range specified for the histogram in this lecture?

• 0 to 512

• 0 to 256

• 0 to 200

• 0 to 100

How can color histograms be used in machine learning?

• To compress images

• To train classifiers for color detection

• To enhance image resolution

• To convert images to grayscale

What does the lecture encourage students to do with different images?

• Plot histograms for different color channels

• Create 3D models

• Develop mobile applications

• Write research papers