Making Machines See Class 12 MCQs: Computer vision is a field of artificial intelligence which can understand the visual data just as humans do. Students can test their knowledge with these below MCQs. Making Machines See Class 12 MCQs are based on the NCERT textbook. We have also collected those MCQs which can be asked in CBSE examinations.
Making Machines See Class 12 MCQ
1. The field of study that helps to develop techniques to help computers “see” is________________.
a. Python
b. Convolution
c. Computer Vision
d. Data Analysis
2. Task of taking an input image and outputting/assigning a class label that best describes the image is __.
a. Image classification
b. Image localization
c. Image Identification
d. Image prioritization
3. Identify the incorrect option
(i) computer vision involves processing and analysing digital images and videos to understand their content.
(ii) A digital image is a picture that is stored on a computer in the form of a sequence of numbers that computers can understand.
(iii) RGB colour code is used only for images taken using cameras.
(iv) Image is converted into a set of pixels and less pixels will resemble the original image.
a. ii
b. iii
c. iii & iv
d. ii & iv
4. The process of capturing a digital image or video using a digital camera, a scanner, or other imaging devices is related to __.
a. Image Acquisition
b. Preprocessing
c. Feature Extraction
d. Detection
5. Which algorithm may be used for supervised learning in computer vision?
a. KNN
b. K-means
c. K-fold
d. KEAM
6. A computer sees an image as a series of _
a. colours
b. pixels
c. objects
d. all of the above
7. __ empowers computer vision systems to extract valuable insights and drive intelligent decision-making in various applications, ranging from autonomous driving to medical diagnostics.
a. Low level processing
b. High insights
c. High-level processing
d. None of the above
8. In Feature Extraction, which technique identifies abrupt changes in pixel intensity and highlights object boundaries?
a. Edge detection
b. Corner detection
c. Texture Analysis
d. boundary detection
9. Choose the incorrect statement related to preprocessing stage of computer vision
a. It enhances the quality of acquired image
b. Noise reduction and Image normalization is often employed with images
c. Techniques like histogram equalization can be applied to adjust the distribution of pixel intensities
d. Edge detection and corner detection are ensured in images.
10. 1 byte = __ bits
a. 10
b. 8
c. 2
d. 1
11. Computer vision can do recognition tasks such as __.
a. Image classification
b. Object detection
c. Facial recognition
d. All of the above
12. When a computer processes an image, it perceives it as a collection of tiny squares known as _.
a. pixels
b. layer
c. vision
d. None of the above
13. The resolution of the image is determined by the number of __ contained in the image.
a. colour
b. pixels
c. layers
d. None of the above
14. In a monochrome image, black and white colours range from _.
a. 255 to 1024
b. 0 to 510
c. 0 to 255
d. None of the above
15. In a monochrome image, the value of 0 corresponds to __.
a. Black
b. White
c. Grey
d. None of the above
16. In a monochrome image, the value of 255 corresponds to __.
a. Black
b. White
c. Grey
d. None of the above
17. __ is the initial stage of computer vision involving the capture of digital images or videos.
a. Image Acquisition
b. Preprocessing
c. Image Normalisation
d. Histogram Equalisation
18. In scientific fields, specialised imaging techniques are used to scan high-detailed images of biological tissues or structures.
a. Magnetic Resonance Image
b. Computer Tomography
c. Both a. and b.
d. None of the above
19. __ in computer vision aims to enhance the quality of the acquired image.
a. Image Acquisition
b. Preprocessing
c. Image Normalisation
d. Histogram Equalisation
20. The techniques used in preprocessing are _.
a. Noise Reduction & Image Normalisation
b. Resizing & Cropping
c. Histogram Equalisation
d. All of the above
21. _ technique used to remove unwanted elements like blurriness, random spots, or distortions in computer vision.
a. Noise Reduction
b. Image Normalisation
c. Histogram Equalisation
d. All of the above
22. Which technique ensures all images in a dataset have a similar scale in computer vision?
a. Noise Reduction
b. Image Normalisation
c. Histogram Equalisation
d. All of the above
23. _ technique helps to adjust the brightness and contrast of an image.
a. Noise Reduction
b. Image Normalisation
c. Histogram Equalisation
d. All of the above
24. _ involves identifying and extracting relevant visual patterns or attributes from the pre-processed image.
a. Noise Reduction
b. Image Normalisation
c. Feature Extraction
d. Histogram Equalisation
25. __ identifies the boundaries between different regions in an image where there is a significant change in intensity.
a. Edge detection
b. Corner detection
c. Texture analysis
d. Colour-based feature extraction
26. identifies points where two or more edges meet. These points are areas of high curvature in an image, focused on identifying sharp changes in image gradients, which often correspond to corners or junctions in objects.
a. Edge detection
b. Corner detection
c. Texture analysis
d. Colour-based feature extraction
27. _ extracts features like smoothness, roughness, or repetition in an image.
a. Edge detection
b. Corner detection
c. Texture analysis
d. Colour-based feature extraction
28. __ quantifies colour distributions within the image, enabling discrimination between different objects or regions based on their colour characteristics.
a. Edge detection
b. Corner detection
c. Texture analysis
d. Colour-based feature extraction
29. _ are fundamental tasks in computer vision, focusing on identifying objects or regions of interest within an image.
a. Detection
b. Segmentation
c. Both a. and b.
d. None of the above
30. KNN stands for __.
a. K. Nearest Neighbour
b. K. New Neighbour
c. K. Nearest New
d. None of the above
31. __ creates a mask around similar characteristic pixels and identifies their class in the given input image.
a. Object Detection
b. Recognition
c. Image segmentation
d. None of the above
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