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2. Differentiate between Computer Vision and Image Processing.
Ans. The difference between Computer Vision and image processing.
Computer Vision Image Processing
Computer Vision enables machines to understand and Image processing involves manipulating and enhancing
interpret visual information, such as identifying objects, images to improve their quality or extract specific
recognising patterns, or making decisions based on visual features. It focuses on the technical manipulation of raw
inputs. It focuses on extracting high-level information to image data.
mimic human vision.
Computer Vision is a superset of image processing. Image processing is a subset of Computer Vision. It means
It means that Computer Vision encompasses image that image processing is one component within the
processing as one of its components or foundational broader domain of Computer Vision. Image processing
steps, but extends beyond it to achieve higher-level tasks provides foundational tools and techniques often used in
like recognition and decision-making. Computer Vision tasks.
It operates at a higher level of abstraction, focusing It operates at a lower level, primarily working with
on deriving meaning from visual inputs and enabling pixel-level data to transform or analyse images without
machines to perform tasks like decision-making. necessarily understanding their content.
3. Describe the application of facial recognition in Computer Vision.
Ans. Facial recognition helps make homes safer and smarter in several ways:
• Security: Smart home systems can recognise family members or regular visitors, allowing them to enter without keys
or codes. This ensures that only trusted people can access your home.
• Visitor records: Smart devices can keep track of visitors by recognising their faces and logging the details. This
makes it easy to see who visited and when.
Schools are also using facial recognition for various tasks:
• Attendance: Instead of calling out names or signing in, students’ faces are scanned to mark attendance automatically.
This saves time and reduces mistakes.
• Access control: Facial recognition ensures that only authorised people can enter certain areas, such as labs or staff
rooms, keeping them secure.
4. What role does Computer Vision play in Self-driving Cars?
Ans. Computer Vision plays a key role in making self-driving cars (also known as autonomous vehicles) possible. These cars
use Computer Vision to "see" and understand the world around them, just like humans use their eyes to observe their
surroundings.
It helps self-driving cars in:
• Identifying objects: Self-driving cars have cameras and sensors that use Computer Vision to detect objects around
them. This includes other vehicles, pedestrians crossings, traffic signs, traffic lights, and even road markings. By
recognising these objects, the car can make safe decisions, such as stopping at a red light or avoiding obstacles.
• Navigating routes: Computer Vision helps the car to determine the optimal route by understanding the road layout
and identifying key points, such as intersections, lanes, and turns. It works along with other technologies like GPS to
ensure the car follows the correct route.
• Monitoring the environment: The car constantly monitors its environment using Computer Vision to track the
distance of surrounding vehicles, ensure the road is clear, or detect any changes that, happen in the surroundings. This
allows the car to respond to unexpected situations, such as an animal crossing the road or sudden changes in weather.
5. What do you mean by Grayscale images?
Ans. Grayscale images are images made up of different shades of gray, ranging from black to white, but without any colour
or hue. It only contains brightness information, meaning they don’t have any colour data (like red, green, or blue). Each
pixel just represents a certain level of brightness or gray, so you don’t see vibrant colours, but you can still see details
in different light or dark areas of the image. The darkest shade is black, representing the complete absence of colour
with a pixel value of 0. The lightest shade is white, indicating the full presence of colour with a pixel value of 255.
In Grayscale images, each pixel is made up of 1 byte (which is 8 bits). This byte stores the pixel’s brightness level, with
values from 0 (black) to 255 (white). A pixel’s value determines how dark or light it appears on the image. The pixels
in a grayscale image are arranged in a 2D grid (a flat array of rows and columns). The image's height (number of rows)
and width (number of columns) define the size of the image. For example, a grayscale image with a height of 100 pixels
and a width of 200 pixels would have 20,000 pixels in total.
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