Page 269 - AI Ver 1.0 Class 10
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Task
Use the given input image matrix and kernel matrix of 3×3 to generate the desired image output matrix:
150 0 255 240 190 25 89 255
100 79 25 0 200 255 67 100
155 145 13 20 0 12 45 0
100 175 0 25 25 15 0 0 –1 0 –1
120 156 255 0 78 56 23 0 × 0 –1 0
115 113 25 90 0 80 56 155 –1 0 –1
135 190 115 116 178 0 145 165
123 255 255 0 255 255 255 0
Fill in the blanks in the output matrix by using the above two-image and kernel matrix.
What is Neural Network?
Neural Networks are a series of algorithms used to recognise hidden patterns in raw data, cluster and classify
it, and continuously learn and improve. They are used in a variety of applications in stock markets, sales and
marketing trends, risk assessment and fraud detection. The main advantage is that the data features can be
extracted automatically by the machine without the input from the developer. Neural networks are primarily
used for solving problems with large datasets, like images.
A Neural Network is divided into multiple layers and each layer is further divided into several blocks called
nodes. Each node is responsible to do its task and pass on it to the next layer. First, we have the input layer which
receives the input in several different formats provided by the programmer and feeds it to the neural network.
No processing occurs in the input layer. The output layer predicts our final output. The output at each node is
called its activation or node value.
What is Convolutional Neural Network (CNN)?
Convolutional Neural Network is a type of an Artificial Neural Network and is made up of neurons that help in
image recognition and image processing. It uses Deep Learning Algorithm that takes an input image, processes
it by assigning learnable weights and biases to various aspects/objects in the image which will help the system to
differentiate one image from the other with maximum accuracy. We can say that Convolutional Neural Network
reduces size, improves features and gives the probability value of the input image. They are trained to identify
and extract the best features from the images.
Computer Vision 267

