Challenge your understanding of SIFT, SURF, and ORB algorithms with this easy-level quiz. Explore fundamental concepts of local feature detection, extraction, and matching in computer vision, including key properties, use cases, and algorithm comparisons.
Which of the following algorithms is specifically designed to be rotation-invariant for feature detection in images?
Explanation: SIFT is designed to be invariant to rotation, making it highly effective for detecting features regardless of image orientation. FAST is a corner detector and is not inherently rotation-invariant. CANNY is an edge detector, not a feature detector, and HAAR features are more related to object detection than keypoint invariance.
Which descriptor is binary and therefore allows for fast matching using Hamming distance?
Explanation: ORB uses binary descriptors, enabling efficient matching via the Hamming distance metric. SIFT and SURF use floating point descriptors, which are slower to compare. GIST is a global descriptor, not specifically used for local feature matching.
Which algorithm is considered the fastest for feature detection and description among SIFT, SURF, and ORB?
Explanation: ORB is specifically designed to provide fast computation in both detection and description phases. SIFT is accurate but comparatively slow, and SURF, though faster than SIFT, is still not as quick as ORB. SALT is not relevant in this context.
Which of the following feature detectors is designed to be robust to changes in illumination?
Explanation: SURF handles illumination changes using the concept of integral images and specific normalization steps. BRIEF is a simple binary descriptor with less robustness to lighting variations. HOG is primarily used for object detection, and FAST lacks built-in robustness to illumination changes.
In the context of image matching, what does the SIFT algorithm use to describe each keypoint?
Explanation: SIFT uses a 128-dimensional floating-point vector to describe each keypoint by summarizing local gradient orientations. A 256-dimensional vector is incorrect; that number does not apply to SIFT. SIFT descriptors are not binary patterns, which are used by ORB, and while SIFT relies on histograms, it is specifically of gradient directions, not edges.
How does SIFT achieve scale invariance when detecting features in images?
Explanation: SIFT constructs a scale-space by progressively blurring and downsampling images, then finds features using the Difference of Gaussians approach. This ensures that features can be detected at multiple scales. Fixed scale thresholding and normalization do not create scale invariance, and edge detection alone does not guarantee it.
Which is a common application of SIFT, SURF, or ORB in computer vision?
Explanation: Feature detection and matching are essential in tasks like image stitching, where images are aligned using keypoints. Data encryption and generating video codecs do not require local feature detectors. Speech synthesis is unrelated to image processing tasks.
Which property is shared by SIFT, SURF, and ORB that helps match features between images taken from different perspectives?
Explanation: All three algorithms are designed to be invariant to scale and rotation, making them effective for matching features across varied viewpoints. They do not rely heavily on color, often operating on grayscale images. Sensitivity to compression is not a primary trait, and fixed orientation assignment is not standard for these methods.
Which algorithm among SIFT, SURF, and ORB uses oriented BRIEF (oBRIEF) for generating its feature descriptors?
Explanation: ORB stands for Oriented FAST and Rotated BRIEF—meaning it modifies BRIEF to add rotational invariance. SIFT and SURF have their own unique descriptor formulations and do not use oBRIEF. FAST is a corner detector, not a descriptor.
What is the preferred distance metric for matching SIFT feature descriptors?
Explanation: Since SIFT descriptors are float vectors, Euclidean distance is used to measure similarity between them. Hamming distance is used for binary descriptors like ORB. Cosine similarity and Manhattan distance are less common for SIFT due to their lower effectiveness in this context.