Quiz 6

A visually appealing illustration of computer vision concepts, including images with keypoints, feature detectors, and graphical representations of algorithms like RANSAC and homography, set in a modern tech-themed environment.

Feature Detection Quiz

Test your knowledge on feature detection and image processing techniques with our engaging quiz. Designed for enthusiasts and professionals alike, this quiz covers essential concepts like feature detection, keypoint matching, and the RANSAC algorithm.

Key Features:

12 challenging questions
Multiple choice and checkbox formats
Immediate feedback on your answers

12 Questions3 MinutesCreated by DetectingStar742

Select all that are involved in feature detection:

Generate a description for each detected point of interest

Detection of points of interest

Find homography

What is the minimum number of point correspondences needed to determine an homography?

Select all that are, usually, involved in creating image panoramas:

Detection of keypoints (i.e. Interest points)

Matching of keypoints using the respective descriptors

Calculating an homography from the matched keypoints

Description of detected keypoints

What is true regarding the Harris corner detector:

It's invariant to scaling

It's invariant to rotation

Its most used component is the keypoint descriptor, not the detector

It's patented

Which are characteristics of a good feature?

Looks pretty

Possesses distinctive properties

Detectable under geometric and intensity changes

Uses a large quantity of information around it

What is true regarding the SIFT feature detector:

Its most used component is the keypoint descriptor, not the keypint detector

It's invariant to rotation

It's patented

It's invariant to scaling

What is true regarding the SIFT feature detector:

Its most used component is the keypoint descriptor, not the keypint detector

It's invariant to rotation

It's patented

It's invariant to scaling

Regarding the "cornerness" measure produced by the Harris corner detector, which indicates that there is NO corner:

R < 0 and |R| is large

R < 0 and |R| is small

R > 0 and |R| is large

R > 0 and |R| is small

What is the "characteristic scale"?

It's a characteristic that all keypoint detectors have

It's the scale at which the "normalized" LoG operator produces the highest peak response when convolved with another signal of approximately the same width

It's the result of applying a descriptor to a keypoint

It's the scale at which the "normalized" LoG operator produces the lowest peak response when convolved with another signal of approximately the same widt

What is the dimension of the homography matrix? Use the character x to indicate multiplication. For example, if it was 10 rows by 20 columns, you'd write 10x20, without spaces.

3x3

5x5

2x2

8x8

RANSAC will attempt several random combinations of point matches in order to select the combination that produces the best model. In each attempt, what is the number of matches is uses?

The minimum number of matches required by the assumed model. For example, stitching 2 images taken by the same camera, slightly rotated about it's optical axis, is modelled by an homography. Therefore, RANSAC will use 4 point correspondences in each attempt.

Less than the number of matches required by the assumed model. For example, stitching 2 images taken by the same camera, slightly rotated about it's optical axis, is modelled by an homography. Therefore, RANSAC will use 2 point correspondences for each attempt.

One more match than the number required by the assumed model. For example, stitching 2 images taken by the same camera, slightly rotated about it's optical axis, is modelled by an homography. Therefore, RANSAC will use 9 point correspondences for each attempt.

The maximum number of matches required by the assumed model. For example, stitching 2 images taken by the same camera, slightly rotated about it's optical axis, is modelled by an homography. Therefore, RANSAC will use 8 point correspondences in each attempt.

What is the threshold in the context of RANSAC?

It's the minimum distance a point that doesn't exactly fit the model will be considered inlier.

It's the maximum distance a point that doesn't exactly fit the model will be considered inlier.

It's the minimum number or inliers required for a model to be selected.

It's the maximum distance a point that doesn't exactly fit the model will be considered outlier.

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