1. What is the purpose of the "im2double" function in MATLAB computer vision?
A) To convert an image to a grayscale representation
B) To normalize the pixel values of an image between 0 and 1
C) To convert an image to a binary representation
D) To perform edge detection on an image
Answer: B) To normalize the pixel values of an image between 0 and 1
Explanation: The "im2double" function in MATLAB computer vision is used to convert an image to a double precision matrix with pixel values normalized between 0 and 1. This is a common preprocessing step for many computer vision algorithms as it helps to ensure that the pixel values are within a consistent range, making it easier to apply various image processing operations. Option A is incorrect because grayscale conversion can be done using the "rgb2gray" function. Option C is incorrect because binary conversion can be done using the "im2bw" function. Option D is incorrect because edge detection can be performed using the "edge" function. .
2: Which of the following methods can be used for image segmentation in computer vision using Matlab?
A) Clustering-based methods
B) Template matching
C) Edge detection
D) All of the above
Answer: D) All of the above
Explanation: Image segmentation is a fundamental task in computer vision, and there are various methods available for achieving it. Matlab offers several tools and functions that can be used for image segmentation, including clustering-based methods, template matching, and edge detection. Each method has its own advantages and disadvantages, and the choice of method depends on the specific application requirements. Therefore, the correct answer is D) All of the above.
3: Which of the following methods can be used to remove noise from an image in computer vision using Matlab?
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A. Median filtering
B. Mean filtering
C. Gaussian filtering
D. All of the above
Answer: D. All of the above
Explanation: In computer vision using Matlab, there are various methods to remove noise from an image, including median filtering, mean filtering, and Gaussian filtering. Median filtering is often used for removing salt-and-pepper noise, while mean filtering is useful for Gaussian noise. Gaussian filtering can also be used for reducing Gaussian noise. All of these filtering methods can be applied in Matlab using built-in functions, such as medfilt2, filter2, and imgaussfilt. Therefore, the correct answer is D. All of the above.
4: Which of the following statements is correct about the "imgradient" function in MATLAB?
A. The "imgradient" function computes the gradient magnitude and direction of a 2D grayscale image.
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B. The "imgradient" function computes the gradient magnitude of a 2D grayscale image.
C. The "imgradient" function computes the gradient magnitude and direction of a 3D grayscale image.
D. The "imgradient" function computes the gradient magnitude and direction of a 2D RGB image.
Answer: A. The "imgradient" function computes the gradient magnitude and direction of a 2D grayscale image.
Explanation:
The "imgradient" function in MATLAB computes the gradient magnitude and direction of a 2D grayscale image. It returns two output arguments, "Gmag" and "Gdir", which correspond to the gradient magnitude and direction, respectively. The gradient magnitude represents the strength of the gradient, and the gradient direction indicates the direction in which the intensity changes the most. This function is commonly used in computer vision applications such as edge detection and feature extraction.
5:Which of the following functions can be used to detect and extract features from an image in MATLAB for computer vision?
A) edge()
B) imnoise()
C) imresize()
D) imrotate()
E) None of the above
Answer:A) edge()
Explanation:
The edge() function in MATLAB can be used to detect edges and features in an image. It uses various edge detection algorithms such as Sobel, Prewitt, Canny, etc., to detect the edges of an image. Other functions such as imnoise(), imresize(), and imrotate() can be used to add noise, resize, and rotate the image, respectively, but they do not extract features from the image. Therefore, the correct answer is A) edge().