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5. How does resizing an image impact pixel information, and why is it important in AI systems?

6. Explain the process of saving an image using the imwrite () function in Jupyter Notebook.
7. What is the role of biases in neural networks, and how are they initialized during network setup?
8. In Convolutional Neural Networks (CNNs), how does the convolution operation contribute to feature
extraction in images?
9. Enlist two smartphone apps that utilize computer vision technology? How have these apps improved
your efficiency or convenience in daily tasks?

10. Briefly describe the purpose of the convolution operator in image processing.
11. How does Computer Vision contribute to the field of autonomous cars?
F. Long answer type questions.
1. Describe the fundamental concepts of images, including pixels, resolution, and pixel value.

2. How does the convolution process work in Computer Vision, and what is the significance of the Kernel
matrix?

3. Discuss the practical applications of Object Detection in the field of Computer Vision and its relevance in
real-world scenarios.

4. Explain the importance of resizing images in image processing and its implications on pixel information.
5. Explore the role of biases in neural networks, their initialization, and why they are crucial in network
setup.
6. What are the different layers in Convolutional Neural Network? What features are likely to be detected
by the initial layers of a neural network and how is it different from what is detected by the later layers?
7. Explain the distinctions between image classification, classification with localization, object detection,
and instance segmentation in computer vision tasks. Provide examples for each to support your answer.
G. Assertion and Reason-based questions.
In each of the following questions, a statement of Assertion (A) is followed by a statement of Reason (R). Observe
both the statements and answer:

(a) If both A and R are true and R is the correct explanation of A
(b) If both A and R are true and R is not the correct explanation of A

(c) If A is true but R is false
(d) If A is false but R is true
1. Assertion (A): Medical imaging is a popular term in the healthcare industry.

Reason (R): Medical imaging aims to create visualizations of the body’s interiors and specific organs or tissues.
2. Assertion (A): Kernel matrices are essential in the convolution process.
Reason (R): A kernel matrix is used to apply effects such as blurring or sharpening in image processing.

3. Assertion (A): Object detection is a more complex task than image classification because it involves identifying
both the presence and location of objects in an image.
Reason (R): Object detection algorithms need to not only classify the objects present in an image but also
accurately localize them by determining their spatial extent.
4. Assertion (A): Grayscale images consist of shades of gray ranging from black to white, where each pixel is
represented by a single byte, and the size of the image is determined by its height multiplied by its width.

Reason (R): Grayscale images are represented using three intensities per pixel, typically ranging from 0 to 255.

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