1. What is Computer Vision?
Answer:
Computer Vision is a field of Artificial Intelligence that enables machines to see, interpret, and understand visual information from the real world such as images and videos. It helps computers to make decisions based on visual inputs, just like humans.
2. How is Computer Vision related to Artificial Intelligence?
Answer:
Computer Vision is a domain of Artificial Intelligence. While AI enables machines to think and make decisions, Computer Vision enables them to “see” and analyze visual data like images or videos to understand what they depict.
3. Differentiate between Computer Vision and Image Processing.
| Computer Vision | Image Processing |
| Helps machines understand and interpret images or videos. | Enhances or transforms images to improve their quality. |
| Focuses on recognizing patterns or objects. | Focuses on improving the image itself. |
| Example: Facial recognition. | Example: Blurring or resizing an image. |
4. Write any four real-life applications of Computer Vision.
Answer:
- Facial Recognition (for phone unlocking or security systems)
- Face Filters (in Snapchat and Instagram)
- Self-Driving Cars (object and lane detection)
- Google Search by Image or Google Translate App
- Medical Imaging (detecting diseases in scans)
5. What are the different tasks performed in Computer Vision?
Answer:
Computer Vision performs several tasks to help machines understand images. The main tasks are:
- Image Classification
- Classification + Localization
- Object Detection
- Image Segmentation
6. What is meant by Image Classification?
Answer:
Image Classification is the process where the computer identifies and assigns an entire image to one label or category.
Example:
Classifying whether an image is of a cat or a dog.
7. What is Classification + Localization?
Answer:
Classification + Localization means the system identifies what the object is and also specifies where it is located within the image.
Example:
Labeling an image as “Dog” and drawing a box around the dog in the image.
8. What is Object Detection?
Answer:
Object Detection involves identifying multiple objects within an image and locating each of them using bounding boxes.
Example:
Detecting and locating cars, people, and traffic lights in a road image.
9. What is Image Segmentation?
Answer:
Image Segmentation divides an image into multiple regions or segments so that each region corresponds to a particular object or part of an object.
Example:
Separating different organs in a medical scan or differentiating sky, trees, and buildings in a landscape photo.
10. What is the difference between single-object and multiple-object tasks in Computer Vision?
| Single Object Task | Multiple Object Task |
| The image contains only one primary object to be identified or analyzed. | The image contains many different objects that need to be detected and classified. |
| Example: Identifying whether a picture is of a cat or a dog. | Example: Detecting cars, pedestrians, and traffic signals in a street image. |
| Uses Image Classification or Classification + Localization. | Uses Object Detection or Image Segmentation. |
11. What is a Pixel?
Answer:
A pixel (picture element) is the smallest unit of an image. Each pixel represents a specific color or brightness value. Many pixels together form an image.
12. What is Image Resolution?
Answer:
Resolution refers to the total number of pixels in an image, usually expressed as width × height (e.g., 1920×1080).
Higher resolution means clearer and more detailed images.
13. What is Pixel Value?
Answer:
Pixel value is the intensity or brightness level of a pixel.
- It ranges from 0 to 255 in an 8-bit image.
- 0 means black and 255 means white.
14. Why is the pixel range from 0 to 255?
Answer:
Each pixel is stored using 8 bits (1 byte), which can represent 2⁸ = 256 possible values (0–255).
15. What are Grayscale Images?
Answer:
Grayscale images display different shades of gray ranging from black (0) to white (255).
Each pixel is represented by a single intensity value.
Example: Black-and-white photographs.
16. How are Grayscale Images stored in computers?
Answer:
They are stored as a 2D array of intensity values, where each value represents the brightness of a pixel between 0 and 255.
17. What are RGB Images?
Answer:
RGB stands for Red, Green, and Blue.
An RGB image is made up of these three color components, and by combining them in different intensities, we can produce a wide range of colors.
18. How do computers store RGB images?
Answer:
An RGB image is stored as three separate color channels —
one for Red, one for Green, and one for Blue.
Each channel contains pixel values from 0 to 255.
19. What is the difference between Grayscale and RGB Images?
| Grayscale Image | RGB Image |
| Contains shades of gray only. | Contains colors created by combining Red, Green, and Blue. |
| Each pixel has one value (intensity). | Each pixel has three values (R, G, B). |
| One channel image. | Three channel image. |
20. What is an RGB Channel?
Answer:
An RGB Channel is a color layer (Red, Green, or Blue) that makes up a color image.
When all three channels are combined, they form the final colored image.
21. What happens when RGB values change?
Answer:
- R=G=B=255 → White
- R=G=B=0 → Black
- R=255, G=0, B=0 → Red
- R=0, G=255, B=0 → Green
- R=0, G=0, B=255 → Blue