Create a computer vision system using decision tree algorithms to solve a real-world problem : Convolutions - Sharpening

The video tutorial introduces convolutions in image processing, highlighting their importance in feature extraction and manipulation. It explains the concept of kernel matrices, which are used to apply effects like blurring and sharpening to images. A practical example demonstrates how convolutions work by scanning an image with a kernel matrix. The tutorial also covers common kernels, such as sharpening and blurring, and their applications in real-world scenarios like Snapchat filters. The session concludes with a preview of further exploration in a Jupyter notebook.

What is one of the daily applications of convolutions mentioned in the video?

• Video streaming

• Text recognition

• Snapchat filters

• Image compression

What is a kernel matrix used for in image processing?

• To apply effects like blurring and sharpening

• To store image metadata

• To increase image resolution

• To convert images to grayscale

In the practical example, what operation is performed between the kernel matrix and the image pixels?

• Division

• Multiplication

• Subtraction

• Addition

What is the result of applying a kernel matrix to an image?

• A new image with modified effects

• A compressed version of the image

• A black and white version of the image

• A higher resolution image

What does a sharpening kernel do to an image?

• Enhances the contrast of certain pixels

• Reduces the image size

• Blurs the image

• Converts the image to grayscale

What is the key element to ensure when applying a blurring kernel?

• The kernel should have a sum of one

• The kernel should be square

• The kernel should have a sum of zero

• The kernel should be larger than the image

Which of the following is NOT a type of kernel mentioned in the video?

• Box blur kernel

• Gaussian blur kernel

• Edge detection kernel

• Sharpening kernel