Page 320 - AI Ver 3.0 class 10_Flipbook
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For example, in the given, if we split the image into
three different channels, namely Red (R), Green R
{G) and Blue (B), the individual layers will have
the following intensity of colours of the individual
pixels. These individual layers when stored in the
memory looks like the image on the extreme right G
i.e. grayscale image because each pixel has a value
intensity of 0 to 255 and as studied earlier, 0 is
considered as black or no presence of colour and
255 means white or full presence of colour. These
three individual RGB values when combined form B
the colour of each pixel.
Additionally, understanding these individual layers is critical for various image processing tasks, such as colour
correction, filtering, or transformations, where manipulating the intensity values of specific channels can
significantly alter the overall appearance of the image. Each pixel in an RGB image, therefore, is not a single value
but a combination of three values that together define its complete colour.
Task 21 st Century #Creativity
Skills
Go to the following link www.piskelapp.com and create your own pixel art. Try and make a GIF using
the online app for your own pixel art.
21 st Century #Media Literacy
Skills
Video Session
Watch the video on "How Computer Vision Applications Work? " at the given Exercise
link:
https://www.youtube.com/watch?v=oGvHtpJMO3M or scan the QR code and answer the Solved Questions
following question:
What do you understand by CNN? SECTION A (Objective Type Questions)
uiz
A. Tick ( ) the correct option.
1. What is the purpose of facial recognition in smart homes?
a. To increase Internet speed
b. To recognise family members for security purposes
Convolutions c. To generate digital filters
d. To track weather changes
Convolutions are mathematical operations used in signal processing and deep learning to filter data. In neural
2. What is the primary goal of Computer Vision?
networks, they involve applying a filter or kernel over input data (like an image) to detect features such as edges or
textures. The output is a transformed version of the data, emphasising relevant features for tasks like classification a. To understand and interpret visual information from the world
or detection. b. To simulate human brain activity
We can create our own convolutions. The way to create the same has been explained in next chapter with the help c. To create 3D models from images
of http://setosa.io/ev/image-kernels/ link. d. To develop algorithms for natural language processing
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