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Computer Vision

Total questions: 10

Worksheet time: 3mins

Name
Class
Date
1.

What is the main purpose of computer vision in robotics systems?

a)

Encrypt communications

b)

Interpret visual data to support decision-making

c)

Simulate robot movements

d)

Store cloud data

2.

What is a pixel in the context of digital images?

a)

A type of filter used to detect edges

b)

The smallest unit of a digital image, representing a single point of color or intensity

c)

A mathematical operation for image segmentation

d)

The depth sensor in stereo vision systems

3.

What is the primary purpose of applying image filters during image processing?

a)

To animate the image for better visual appeal

b)

To reduce noise and enhance important features

c)

To convert images into text

d)

To randomly change pixel values

4.

Which filter is most effective for removing salt-and-pepper noise from an image?

a)

Mean filter

b)

Gaussian filter

c)

Median filter

d)

Bilateral filter

5.

What is the output of an image segmentation algorithm using thresholding?

a)

A color-enhanced image

b)

A binary image separating background and object

c)

An animated gif

d)

A compressed image

6.

In OpenCV, which function is used to convert an image from BGR to Grayscale?

a)

cv2.grayConvert()

b)

cv2.imageToGray()

c)

cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

d)

cv2.colorGray()

7.

Which operation removes pixels from the object boundary in binary images?

a)

Dilation

b)

Erosion

c)

Thresholding

d)

Contouring

8.

. Which algorithm applies two masks (horizontal and vertical) to estimate gradient intensity in an image?

a)

Laplacian

b)

Hough Transform

c)

Sobel

d)

Fourier Transform

9.

What is the first step in the Canny edge detection algorithm?

a)

Calculating gradient direction

b)

Applying a Gaussian filter to reduce noise

c)

Hysteresis thresholding

d)

Converting to binary image

10.

How does the Canny algorithm ensure edges are clean and precise during the third stage?

a)

By compressing the image

b)

By combining all channels into one

c)

By suppressing non-maximum gradients

d)

By randomizing pixel values