Feature Detection Intro MCQ
What makes a good keypoint, descriptor vectors, nearest-neighbor matching, ratio test, and geometric verification basics.
Keypoints
Locations
Descriptors
Vectors
Matching
NN / ratio
Invariance
Scale / rot
Local features
Many pipelines detect repeatable interest points, compute appearance descriptors, match across views, then estimate geometry (e.g., homography, essential matrix) with RANSAC.
Detector vs descriptor
Detectors propose (x,y,scale,orientation); descriptors encode local patch appearance for discrimination.
Vocabulary
Corners & blobs
Corners have two-direction intensity change; blobs respond to maxima of scale-space filters.
Distance metrics
L2 for float descriptors; Hamming for binary (ORB/BRIEF).
Lowe’s ratio
Reject ambiguous matches when nearest vs second-nearest distance ratio is high.
RANSAC
Fits a model while rejecting outliers in putative correspondences.
Typical flow
Detect → Describe → Match → Geometric verify