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Computer Vision
Image Processing
Feature Detection
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Feature Detection Quiz Questions
1.
What is the main advantage of using a binary feature descriptor, such as BRIEF, in feature detection?
A. Improved color representation
B. Robustness to rotation
C. Reduced memory usage and computation speed
D. Enhanced noise reduction
view answer:
C. Reduced memory usage and computation speed
Explanation:
Binary feature descriptors like BRIEF offer reduced memory usage and faster computation.
2.
Which feature descriptor is used to capture the local patterns and relationships between pixels in an image?
A. LBP (Local Binary Pattern)
B. Sobel operator
C. SIFT (Scale-Invariant Feature Transform)
D. Radon transform
view answer:
A. LBP (Local Binary Pattern)
Explanation:
LBP (Local Binary Pattern) captures local patterns and pixel relationships.
3.
In image processing, what term is used to describe the process of finding distinctive regions in an image that can be used for matching or recognition?
A. Histogram equalization
B. Feature detection
C. Image resizing
D. Edge detection
view answer:
B. Feature detection
Explanation:
Feature detection is the process of finding distinctive regions in an image for matching and recognition.
4.
Which algorithm is suitable for detecting interest points in an image and is used in feature matching for object tracking and image stitching?
A. Histogram equalization
B. Canny edge detector
C. FAST (Features from Accelerated Segment Test)
D. Median filtering
view answer:
C. FAST (Features from Accelerated Segment Test)
Explanation:
FAST is suitable for detecting interest points and is used in feature matching for object tracking and image stitching.
5.
What does the acronym "BRIEF" stand for in computer vision?
A. Binary Robust Invariant Efficient Features
B. Basic Robust Image Enhancement Framework
C. Blockwise Region Invariant Extraction Function
D. Binary Robust Independent Elementary Features
view answer:
A. Binary Robust Invariant Efficient Features
Explanation:
BRIEF stands for Binary Robust Invariant Efficient Features, a binary feature descriptor.
6.
Which feature detection algorithm is known for its robustness to image transformations and changes in lighting conditions?
A. Gabor filter
B. Histogram equalization
C. ORB (Oriented FAST and Rotated BRIEF)
D. Harris Corner Detector
view answer:
C. ORB (Oriented FAST and Rotated BRIEF)
Explanation:
ORB is known for its robustness to image transformations and lighting changes.
7.
Which feature detection and description technique is used for characterizing the local intensity variations in an image by analyzing the gradients?
A. Histogram equalization
B. Canny edge detector
C. HOG (Histogram of Oriented Gradients)
D. Radon transform
view answer:
C. HOG (Histogram of Oriented Gradients)
Explanation:
HOG is used to characterize local intensity variations by analyzing gradients.
8.
What is the primary objective of the RANSAC (Random Sample Consensus) algorithm in computer vision?
A. Finding correspondence between key points in images
B. Reducing image noise
C. Detecting edges
D. Removing image colors
view answer:
A. Finding correspondence between key points in images
Explanation:
RANSAC is used to find correspondences between key points in images, typically for geometric transformations.
9.
Which feature detection algorithm is known for its resistance to noise and is used for detecting key points in images?
A. Sobel operator
B. Gabor filter
C. Harris Corner Detector
D. Median filtering
view answer:
C. Harris Corner Detector
Explanation:
The Harris Corner Detector is known for its resistance to noise and is used to detect key points.
10.
What is the main goal of image feature detection and description in computer vision?
A. Enhancing image colors
B. Reducing image resolution
C. Identifying and describing distinctive points or regions in images
D. Eliminating image noise
view answer:
C. Identifying and describing distinctive points or regions in images
Explanation:
The main goal of feature detection and description is to identify and describe distinctive points or regions in images for various computer vision tasks.
11.
Which feature detection algorithm is often used in image stitching applications to find overlapping regions in images?
A. Canny edge detector
B. BRISK (Binary Robust Invariant Scalable Keypoints)
C. Median filtering
D. Histogram equalization
view answer:
B. BRISK (Binary Robust Invariant Scalable Keypoints)
Explanation:
BRISK is often used in image stitching applications to find overlapping regions in images for panoramic stitching.
12.
What is the primary purpose of the Canny edge detector in image processing?
A. Noise reduction
B. Image resizing
C. Edge detection
D. Color correction
view answer:
C. Edge detection
Explanation:
The Canny edge detector is used to identify edges in images. It locates points in an image where there is a rapid change in intensity, typically corresponding to object boundaries.
13.
Which of the following techniques is used for image smoothing and reducing noise in images?
A. Histogram equalization
B. Median filtering
C. Contrast stretching
D. Sobel operator
view answer:
B. Median filtering
Explanation:
Median filtering is a technique used to reduce noise in images. It replaces the pixel value at the center of a neighborhood with the median value of the pixels in that region.
14.
What does the acronym "SURF" stand for in computer vision?
A. Speeded-Up Robust Features
B. Simple Underlying Recognition Framework
C. Scale-Invariant Unifying Region Features
D. Superfast Unsupervised Recognition Function
view answer:
A. Speeded-Up Robust Features
Explanation:
SURF stands for Speeded-Up Robust Features, a feature detection and description algorithm.
15.
Which algorithm is known for its ability to detect corners in images and is used in many feature detection applications?
A. SIFT (Scale-Invariant Feature Transform)
B. HOG (Histogram of Oriented Gradients)
C. ORB (Oriented FAST and Rotated BRIEF)
D. Gabor filter
view answer:
A. SIFT (Scale-Invariant Feature Transform)
Explanation:
SIFT is known for detecting and describing key points, including corners, in images.
16.
What is the main advantage of using the ORB (Oriented FAST and Rotated BRIEF) algorithm for feature detection?
A. Speed and efficiency
B. Robustness to scale changes
C. Superior color correction
D. Complex pattern recognition
view answer:
A. Speed and efficiency
Explanation:
ORB is known for its speed and efficiency in feature detection.
17.
Which descriptor is often used in combination with the SIFT algorithm for feature matching and object recognition?
A. Histogram of Oriented Gradients (HOG)
B. Binary Robust Invariant Scalable Keypoints (BRISK)
C. Radon transform
D. Wavelet transform
view answer:
B. Binary Robust Invariant Scalable Keypoints (BRISK)
Explanation:
BRISK is often used in combination with SIFT for feature matching and object recognition.
18.
What is the main purpose of a feature descriptor in computer vision?
A. To reduce image noise
B. To resize images
C. To detect edges
D. To provide a compact representation of a feature's characteristics
view answer:
D. To provide a compact representation of a feature's characteristics
Explanation:
Feature descriptors provide a compact representation of a feature's characteristics for matching and recognition.
19.
Which feature detection and description algorithm is particularly well-suited for tracking objects in video sequences?
A. FAST (Features from Accelerated Segment Test)
B. Gabor filter
C. Histogram equalization
D. Canny edge detector
view answer:
A. FAST (Features from Accelerated Segment Test)
Explanation:
FAST is suitable for real-time object tracking in video sequences.
20.
What is the primary purpose of the Harris Corner Detector in computer vision?
A. Edge detection
B. Corner detection
C. Image resizing
C. Corner detection
Explanation:
Answer: B
21.
Which of the following algorithms is often used for matching key points between two images and finding correspondences?
A. Histogram equalization
B. RANSAC (Random Sample Consensus)
C. Sobel operator
D. Contrast stretching
view answer:
B. RANSAC (Random Sample Consensus)
Explanation:
RANSAC is commonly used for matching key points and finding correspondences between images.
22.
Which technique is used to extract local texture information from an image and is often used in texture classification?
A. Histogram equalization
B. Gabor filter
C. Median filtering
D. Canny edge detector
view answer:
B. Gabor filter
Explanation:
The Gabor filter is used to extract local texture information from an image and is employed in texture classification.
23.
What is the primary goal of feature detection in computer vision?
A. Reducing image resolution
B. Enhancing image colors
C. Locating distinctive points or regions in an image
D. Eliminating image noise
view answer:
C. Locating distinctive points or regions in an image
Explanation:
The primary goal of feature detection is to locate distinctive points or regions in an image for various computer vision tasks.
24.
Which feature detection and description algorithm is known for its invariance to rotation and scaling?
A. Gabor filter
B. SIFT (Scale-Invariant Feature Transform)
C. Histogram equalization
D. Sobel operator
view answer:
B. SIFT (Scale-Invariant Feature Transform)
Explanation:
SIFT is known for its invariance to rotation and scaling.
25.
What is the primary advantage of using the FAST (Features from Accelerated Segment Test) algorithm for feature detection?
A. Robustness to scale changes
B. Real-time performance
C. Precise edge detection
D. Color correction
view answer:
B. Real-time performance
Explanation:
FAST is known for its real-time performance in feature detection.
26.
Which of the following is a common feature descriptor used for texture analysis in images?
A. Harris Corner Detector
B. Histogram equalization
C. LBP (Local Binary Pattern)
D. Radon transform
view answer:
C. LBP (Local Binary Pattern)
Explanation:
LBP (Local Binary Pattern) is a common feature descriptor used for texture analysis.
27.
In computer vision, what is the "scale-invariance" property of a feature descriptor?
A. The feature can be resized without changing its appearance
B. The feature is robust to color variations
C. The feature is insensitive to noise
D. The feature is rotationally invariant
view answer:
A. The feature can be resized without changing its appearance
Explanation:
A scale-invariant feature descriptor can be resized without changing its appearance, making it robust to changes in scale.
28.
Which algorithm is commonly used for detecting and matching keypoints in a pair of images to find the perspective transformation between them?
A. Histogram equalization
B. RANSAC (Random Sample Consensus)
C. Canny edge detector
D. Sobel operator
view answer:
B. RANSAC (Random Sample Consensus)
Explanation:
RANSAC is used to find the perspective transformation between images by detecting and matching keypoints.
29.
Which feature detection algorithm is known for its simplicity and efficiency in finding corner-like features?
A. SIFT (Scale-Invariant Feature Transform)
B. BRISK (Binary Robust Invariant Scalable Keypoints)
C. Histogram equalization
D. Median filtering
view answer:
B. BRISK (Binary Robust Invariant Scalable Keypoints)
Explanation:
BRISK is known for its simplicity and efficiency in finding corner-like features.
30.
Which descriptor is commonly used in object recognition to represent the local spatial arrangement of gradients in an image?
A. Gabor filter
B. SIFT (Scale-Invariant Feature Transform)
C. Histogram equalization
D. HOG (Histogram of Oriented Gradients)
view answer:
D. HOG (Histogram of Oriented Gradients)
Explanation:
HOG is used to represent the local spatial arrangement of gradients in images, often for object recognition.
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