Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

IACV_EXAM > Pre-deep learning > Flashcards

Pre-deep learning Flashcards

1
Q

What is the primary goal of Principal Component Analysis (PCA)?

A) To increase the number of features in the dataset
B) To reduce the dimensionality of the dataset while retaining most of the variance
C) To perform image segmentation
D) To apply convolution to the dataset

A

B) To reduce the dimensionality of the dataset while retaining most of the variance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Which of the following is a global feature descriptor used in image analysis?

A) SIFT
B) HOG
C) Color histogram
D) SURF

A

C) Color histogram

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What is the main advantage of using local features like SIFT or SURF in image processing?

A) They are invariant to scale, rotation, and illumination changes
B) They are simple to compute
C) They require no preprocessing
D) They reduce the image to a single feature vector

A

A) They are invariant to scale, rotation, and illumination changes

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

In the context of color spaces, what does HSV stand for?

A) Hue, Saturation, Value
B) Hue, Sharpness, Vibrance
C) High, Standard, Vivid
D) Hue, Saturation, Vividness

A

A) Hue, Saturation, Value

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Which of the following is NOT a property of PCA?

A) Orthogonality of principal components
B) Maximizing variance along the axes
C) Minimizing the reconstruction error
D) Maximizing correlation between components

A

D) Maximizing correlation between components

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What technique is commonly used to extract texture features from an image?

A) Histogram equalization
B) Co-occurrence matrices
C) Gaussian filtering
D) PCA

A

B) Co-occurrence matrices

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What is the purpose of using the Lab* color space in image processing?

A) To separate the luminance and chromaticity components for better color manipulation
B) To represent images in binary format
C) To enhance the texture features
D) To perform edge detection

A

A) To separate the luminance and chromaticity components for better color manipulation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

How does the Gabor filter function in texture analysis?

A) It enhances edges in the image
B) It captures both spatial and frequency information for texture representation
C) It performs histogram equalization
D) It reduces the image noise

A

B) It captures both spatial and frequency information for texture representation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What is the main use of eigenvalues and eigenvectors in PCA?

A) To perform image convolution
B) To compute the Fourier transform
C) To identify the principal components and their significance
D) To segment the image

A

C) To identify the principal components and their significance

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Which feature descriptor is based on the distribution of gradient orientations in localized portions of an image?

A) SIFT
B) HOG (Histogram of Oriented Gradients)
C) LBP (Local Binary Patterns)
D) SURF

A

B) HOG (Histogram of Oriented Gradients)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Question: PCA reduces the dimensionality of data by projecting it onto the eigenvectors corresponding to the largest eigenvalues.

A) True
B) False

A

A) True

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Local Binary Patterns (LBP) are a texture descriptor that captures the spatial structure of local image texture.

A) True
B) False

A

A) True

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

The RGB color space is often preferred over HSV for color-based segmentation tasks.

A) True
B) False

A

B) False

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

The primary advantage of using PCA is that it makes the data more interpretable by maximizing the variance along the principal components.

A) True
B) False

A

A) True

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

The co-occurrence matrix method is used to extract shape features from an image.

A) True
B) False

A

B) False

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

The SIFT algorithm is robust to changes in scale and rotation but not to illumination changes.

A) True
B) False

A

B) False

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

In the Lab* color space, ‘L’ represents the lightness component, while ‘a’ and ‘b*’ represent the color-opponent dimensions.

A) True
B) False

A

A) True

18
Q

Gabor filters are used for texture analysis because they provide a joint representation of spatial and frequency information.

A) True
B) False

A

A) True

19
Q

Eigenvectors in PCA are orthogonal to each other.

A) True
B) False

A

A) True

20
Q

The principal components in PCA are the directions in which the variance of the data is maximized.

A) True
B) False

A

A) True

21
Q

Which of the following algorithms is used for edge detection by calculating the gradient of the image intensity?

A) SIFT
B) Canny
C) HOG
D) SURF

A

B) Canny

22
Q

In image processing, what is the primary purpose of the Sobel operator?

A) Texture analysis
B) Color segmentation
C) Edge detection
D) Image smoothing

A

C) Edge detection

23
Q

What does the term “eigenface” refer to in facial recognition?

A) A method for facial landmark detection
B) The eigenvectors used in PCA for face recognition
C) A filter used in image preprocessing
D) A type of convolutional filter

A

B) The eigenvectors used in PCA for face recognition

24
Q

Which feature extraction method is specifically designed to be invariant to affine transformations?

A) SIFT
B) HOG
C) LBP
D) PCA

A

A) SIFT

25
Q

What does the term “texture” in an image refer to?

A) The color composition of an image
B) The variation in pixel intensity
C) The spatial arrangement of pixel intensities
D) The edges within an image

A

C) The spatial arrangement of pixel intensities

26
Q

Which of the following color spaces is perceptually uniform, meaning that a change of the same amount in a color value should produce a change of about the same visual importance?

A) RGB
B) HSV
C) Lab*
D) CMYK

A

C) Lab*

27
Q

Which method of feature extraction involves computing a histogram of gradient orientations within a localized region of an image?

A) HOG
B) SIFT
C) SURF
D) LBP

A

A) HOG

28
Q

What is the main purpose of using color histograms in image analysis?

A) To enhance image resolution
B) To represent the color distribution of an image
C) To detect edges
D) To perform texture analysis

A

B) To represent the color distribution of an image

29
Q

Which feature descriptor is based on comparing each pixel with its neighbors and encoding the result as a binary pattern?

A) HOG
B) SIFT
C) LBP (Local Binary Patterns)
D) SURF

A

C) LBP (Local Binary Patterns)

30
Q

In PCA, the principal components are the eigenvectors of which matrix?

A) The image matrix
B) The covariance matrix of the data
C) The Fourier transform of the image
D) The gradient matrix

A

B) The covariance matrix of the data

31
Q

The RGB color space is often preferred for tasks involving color manipulation and color-based segmentation due to its perceptual uniformity.

A) True
B) False

A

B) False

32
Q

The Gabor filter is primarily used for edge detection in image processing.

A) True
B) False

A

B) False

33
Q

Local Binary Patterns (LBP) is a texture descriptor that is rotation invariant.

A) True
B) False

A

B) False

34
Q

The principal components in PCA are orthogonal to each other, ensuring that each component captures unique variance in the data.

A) True
B) False

A

A) True

35
Q

Co-occurrence matrices are used to capture color information in an image.

A) True
B) False

A

B) False

36
Q

Histogram equalization is a technique used to improve the contrast of an image by adjusting the intensity distribution.

A) True
B) False

A

A) True

37
Q

PCA can be used for noise reduction by projecting the data onto the principal components and reconstructing it with fewer components.

A) True
B) False

A

A) True

38
Q

The SIFT algorithm detects and describes local features in images, making it robust to scale and rotation changes.

A) True
B) False

A

A) True

39
Q

The term “eigenvalue” in PCA represents the variance captured by its corresponding eigenvector.

A) True
B) False

A

A) True

40
Q

The HSV color space is often used for tasks that involve human perception of colors, as it separates color information (hue) from intensity (value).

A) True
B) False

A

A) True

IACV_EXAM (4 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions