SIFT (Scale-Invariant Feature Transform) MCQ
Gaussian pyramid, DoG extrema, subpixel refinement, gradient orientation histograms, and 4×4×8 descriptor layout.
Pyramid
σ octaves
DoG
G1−G2
Orientation
8-bin hist
128-D
4×4×8
SIFT overview
SIFT finds scale-space extrema of Difference-of-Gaussian, refines location/scale, assigns dominant orientation(s), then samples gradient histograms into a 128-dimensional vector.
Why DoG?
DoG approximates scale-normalized LoG extrema—efficient multi-scale blob/corner detection.
Stages
Extrema
Compare pixel to 26 neighbors in scale-space cube; reject low contrast / edge-like points.
Orientation
Histogram of gradient orientations weighted by magnitude; peaks create multiple oriented features.
Descriptor
4×4 spatial grid × 8 orientation bins, normalized for illumination robustness.
Patents / use
Historically patent encumbered; now widely usable in research and OpenCV—know licensing for products.
Descriptor layout
16 cells × 8 directions → 128 values (with truncation/normalization steps)