Page 218 - Ai_C10_Flipbook
P. 218
What did you observe?
The result will be the opposite of the positive values. The image may
darken or show an inverse effect. Negative values tend to blur or
edge-detect the image, creating darker or more shadowed areas.
• Create a convolution matrix that includes both positive and
negative values, for example:
What did you observe?
This creates a mix of effects on the image. The positive and negative values in the kernel work together to
produce a more complex transformation. You might notice sharpening, edge detection, or contrast changes in
the image. The result depends on the weight of the positive and negative values and how they interact with the
pixel values in the image.
Task 21 st Century #Technology Literacy
Skills
Visit the https://setosa.io/ev/image-kernels/ link and change the value in the given image kernel.
Now answer the following questions:
1. Make 4 numbers negative. Keep the rest as it is. Write your observation:
2. Now, change the center value to negative. Write your observation:
3. Now, change the second value of each row to positive. Keep the rest
as it is. Write your observation:
4. Now, what effect did you apply to final image?
What is Neural Network?
Neural Networks are a series of algorithms used to recognise hidden patterns in raw data, process it, cluster
and classify it, and continuously learn and improve. They are used in a variety of applications such as predicting
stock prices, identifying sales and marketing trends, risk assessment, and fraud detection. The main advantage of
neural network 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 units or neurons,
also known as nodes Each neuron processes its inputs, applies a mathematical function and passes the result 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. Minimal processing occurs in the input layer, as it simply passes
the raw input data forward. The output layer produces the final prediction or decision based on the learned
patterns. 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 Artificial Neural Network and is made up of neurons that help in image
recognition and image processing. It uses a deep learning algorithm that takes an input image, processes it by assigning
learnable weights and biases to various aspects/objects in the image, enabling the network to identify patterns and
features helping the system differentiate one image from the other with maximum accuracy. CNNs reduce the spatial
dimensions (size) of the input through operations like pooling, while retaining the essential features and give the predicted
class probabilities for the input image. They are trained to identify and extract the best features from the images.
216 Artificial Intelligence Play (Ver 1.0)-X

