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•  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.

                 C.  Competency-based/Application-based questions.                               21 st  Century   #Critical Thinking
                                                                                                     Skills  #Flexibility
                    1.  You are developing an AI system for financial risk assessment in a stock market context. Which application of neural
                       networks is most relevant to your project, allowing you to recognise hidden patterns and classify data effectively?

                       a. Sales and marketing trends        b. Fraud detection
                       c. Grayscale image processing        d. Input data conversion
                   Ans.  b. Fraud detection
                    2.  Google Search by Image feature is used to find a similar image. How does Computer Vision process the uploaded
                       image to deliver results?
                  Ans.  Computer Vision algorithms extract key features like shapes and patterns, compare them with a database, and provide
                       matching results.



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