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7. Pixel stands for ……….…….................
8. ……….……................ is the process of identifying or detecting the instances of real-world objects.
9. An image comprises of a rectangular array of dots known as ……….…….................
10. ……….……................ means where the object is located in the image.
C. State whether these statements are true or false.
1. AI enables machines to observe, process, and act upon information like humans. ……….……................
2. Computer Vision helps machines perceive the world through images or visual data. ……….……................
3. Image Processing is a superset of Computer Vision. ……….……................
4. Face filters in Computer Vision only detect and track facial features but do not apply digital effects. ……….……................
5. Grayscale images are simpler to process and take up less memory compared to colour images. ……….……................
6. Computer Vision only works with raw image data and does not require advanced algorithms
for interpretation. ……….……................
7. Computer Vision can help self-driving cars identify objects like pedestrians, traffic signs, and
other vehicles. ……….……................
8. Computer Vision operates at a lower level of abstraction than Image Processing. ……….……................
SECTION B (Subjective Type Questions)
A. Short answer type questions.
1. What is the concept of Computer Vision?
Ans. Computer Vision means giving the ability to the computer to see the world just like humans. It is a domain of Artificial
Intelligence that enables computers to see, observe and understand digital images or data, process them by acquiring,
screening, analysing, identifying and extracting information using the machine learning and neural network algorithms.
2. What is the role of facial recognition in Computer Vision?
Ans. Facial recognition is a technology that uses Computer Vision to identify and verify people based on their facial features.
It has become an important part of smart cities and smart homes, making life more convenient, secure and efficient.
3. How are face filters used in Computer Vision?
Ans. Face filters in Computer Vision are fun and interactive tools that uses algorithm to detect and track facial features, such
as the eyes, nose, mouth, and overall face shape. This process is called facial landmark detection. These filters then
overlay digital effects or objects on a person's face in real time, such as hats, glasses, or funny faces. They are commonly
used in social media apps like Instagram and Snapchat, video conferencing platforms, and Augmented Reality (AR)
applications.
4. What is classification in Computer Vision?
Ans. Classification is the process of determining the class or category of an input image. The input image is analysed using
a machine learning algorithm, which assigns it to one of the predefined categories. These categories are established
by training the algorithm on a set of labelled sample images. This foundational task in Computer Vision has numerous
practical applications, such as medical diagnostics, object recognition, and autonomous vehicles.
5. What are multiple object tasks in Computer Vision?
Ans. Multiple object tasks involve providing multiple images as input to the Computer Vision system. It can be further
divided into two categories i.e. object detection and instance segmentation.
Computer Vision (Theory) 321

