Computer Vision – Class 10 Artificial Intelligence NCERT Book Solutions
I. Multiple Choice Questions:
Que 1. What is the primary objective of the Convolution Layer in a Convolutional Neural Network (CNN)?
A) To flatten the input image
B) To assign importance to various aspects/objects in the image
C) To reduce the spatial size of the input image
D) To perform element-wise multiplication of image arrays
Answer: B) To assign importance to various aspects/objects in the image
Que 2. Which of the following tasks is an example of computer vision?
A) Rescaling an image
B) Correcting brightness levels in an image
C) Object detection in images or videos
D) Changing tones of an image
Answer: C) Object detection in images or videos
Que 3. How is resolution typically expressed?
A) By the number of pixels along the width and height, such as 1280×1024
B) By the brightness level of each pixel, ranging from 0 to 255
C) By the total number of pixels, such as 5 megapixels
D) By the arrangement of pixels in a 2-dimensional grid
Answer: A) By the number of pixels along the width and height, such as 1280×1024
Que 4. What is the core task of image classification?
A) Identifying objects and their locations in images
B) Segmenting objects into individual pixels
C) Assigning an input image one label from a fixed set of categories
D) Detecting instances of real-world objects in images
Answer: C) Assigning an input image one label from a fixed set of categories
Que 5. What is the function of the Rectified Linear Unit (ReLU) layer in a CNN?
A) To reduce the image size for more efficient processing
B) To assign importance to various aspects/objects in the input image
C) To get rid of negative numbers in the feature map and retain positive numbers
D) To perform the convolution operation on the input image
Answer: C) To remove negative numbers in the feature map and keep only positive numbers
Que 6. Object detection and handwriting recognition are examples of tasks commonly associated with:
A) Computer vision
B) Image processing
C) Both computer vision and image processing
D) Neither computer vision nor image processing
Answer: A) Computer vision
Que 7. What does the pixel value represent in an image?
A) Width of the pixel
B) Brightness or color of the pixel
C) Height of the pixel
D) Resolution of the pixel
Answer: B) Brightness or color of the pixel
Que 8. In the byte image format, what is the range of possible pixel values?
A) 0 to 10
B) 0 to 100
C) 0 to 1000
D) 0 to 255
Answer: D) 0 to 255
Que 9. In a grayscale image, what does the darkest shade represent?
A) Total presence of color
B) Zero value of pixel
C) Lightest shade of gray
D) Maximum pixel value
Answer: B) Zero value of pixel (black / darkest shade)
Que 10. In an RGB image, what does a pixel with an intensity value of 0 represent?
A) Full presence of color
B) No presence of color
C) Maximum brightness level
D) Minimum brightness level
Answer: B) No presence of color
Assertion and reasoning-based questions:
Que 11. Assertion: Object detection is a more complex task than image classification because it involves identifying both the presence and location of objects in an image.
Reasoning: Object detection algorithms need to not only classify the objects present in an image but also accurately localize them by determining their spatial extent.
Select the appropriate option for the statements given above:
A) Both A and R are true and R is the correct explanation of A
B) Both A and R are true and R is not the correct explanation of A
C) A is true but R is false
D) A is False but R is true
Answer: A) Both A and R are true and R is the correct explanation of A
Que 12. Assertion: 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.
Reasoning: Grayscale images are represented using a three intensities per pixel, typically ranging from 0 to 255.
Select the appropriate option for the statements given above:
A) Both A and R are true and R is the correct explanation of A
B) Both A and R are true and R is not the correct explanation of A
C) A is true but R is false
D) A is False but R is true
Answer: C) A is true but R is false
Reflection Time:
Que 1. Imagine you have a smartphone camera app that can recognize objects. When you point your camera at a dog, the app identifies it as a dog, analyzing patterns and features in the image. Behind the scenes, the app’s software processes the image, detecting edges, shapes, and colors, then compares these features to a vast database to make accurate identifications.” Identify the technology used in the above scenario and explain the way it works.
Answer: The technology is Computer Vision using AI.
The camera captures the image, the system detects shapes, edges, textures, and colors, extracts features, compares them with trained data, and then identifies the object as a dog.
Que 2. Enlist two smartphone apps that utilize computer vision technology? How have these apps improved your efficiency or convenience in daily tasks?
Answer: Two smartphone apps using computer vision
- Google Lens
- Face Unlock / Face ID
They help by making tasks faster, like identifying objects, translating text, or unlocking the phone securely.
Que 3. How an RGB image is different from a grayscale image?
Answer: RGB vs Grayscale image
- Grayscale image → only shades of gray, one value per pixel (0–255).
- RGB image → three values per pixel (Red, Green, Blue), used to form colors.
Que 4. Determine the color of a pixel based on its RGB values mentioned below: (i) R=0, B=0, G=0 (ii) (iii) R=255, B=255, G=255 R=0, B=0, G=255 (iv) R=0, B=255, G=0
Answer:
(i) R=0, G=0, B=0 → Black
(ii) R=255, G=255, B=255 → White
(iii) R=0, G=0, B=255 → Blue
(iv) R=0, G=255, B=0 → Green
Que 5. Briefly describe the purpose of the convolution operator in image processing.
Answer: The convolution operator helps to detect features in an image such as edges, lines, textures, and patterns.
Que 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?
Answer: Layers in CNN & features detected
Layers:
- Convolution layer
- ReLU layer
- Pooling layer
- Fully connected layer
Early layers detect simple features (edges, lines, shapes).
Later layers detect complex features (objects, faces, patterns).
Que 7. “Imagine you’re a researcher tasked with improving workplace safety in a manufacturing environment. You decide to employ computer vision technology to enhance safety measures.”
- How would you utilize computer vision in two distinct applications to promote safety within the manufacturing plant, ensuring both the physical well-being of employees and the efficiency of operations?
- Provide detailed explanations for each application, including the specific computer vision techniques or algorithms you would employ, and how they would contribute to achieving your safety goals.
Answer: Two applications:
- Helmet / safety gear detection
- Detects whether workers are wearing helmets or jackets.
- Helps prevent accidents.
- Machine area monitoring
- Detects if a person enters a restricted or dangerous zone.
- Triggers alerts to avoid injury.
Computer vision uses object detection, pattern recognition, and real-time monitoring to improve safety and efficiency.
Que 8. 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.
Answer: Differences between CV tasks
- Image classification → Gives one label to an image
Example: “Cat” - Classification with localization → Label + location (bounding box around one object)
- Object detection → Detects many objects with boxes
Example: cars, people in a street image - Instance segmentation → Detects and outlines each object pixel-wise
Case study-based questions:
Que 9. “Agriculture is an industry where precision and efficiency are crucial for sustainable production. Traditional farming methods often rely on manual labor and visual inspection, which can be time-consuming and error-prone. However, advancements in computer vision technology offer promising solutions to optimize various agricultural processes. Agricultural drones equipped with high-resolution cameras and computer vision algorithms are increasingly being used to monitor crop health, detect diseases, and assess crop yields.”
Answer the following questions based on the case study mentioned above:
(a) How does the integration of computer vision technology with drones improve efficiency in agricultural practices compared to traditional methods?
(b) What are some key indicators or parameters that computer vision algorithms can analyze to assess crop health and detect diseases?
Answer: (a) How efficiency improves
Computer vision with drones:
- Covers large fields quickly
- Reduces manual inspection
- Provides accurate and timely analysis
(b) Parameters analyzed
- Leaf color and texture
- Plant health
- Disease spots
- Crop density
- Moisture stress
Que 10. You are tasked with developing a computer vision system for a self-driving car company. The system needs to accurately detect and classify various objects on the road to ensure safe navigation. Imagine you’re working on improving the object detection algorithm for the self-driving car’s computer vision system. During testing, you notice that the system occasionally misclassifies pedestrians as cyclists, especially in low-light conditions.
How would you approach addressing this issue? What steps would you take to enhance the accuracy of pedestrian detection while ensuring the system’s overall performance and reliability on the road?
Answer: To improve accuracy:
- Add more training images in low-light
- Use data augmentation (shadows, night images)
- Improve feature extraction
- Use better object detection models
- Combine camera data with sensors like LiDAR
- Perform continuous testing and retraining
By Anjeev Kr Singh – Computer Science Educator
Published on : January 6, 2026 | Updated on : January 6, 2026
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