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Harris Corner Detector MCQ
Second-moment matrix M, eigenvalue interpretation, response R = det(M) − k·trace²(M), and why edges score low.
M matrix
2×2 tensor
λ₁, λ₂
Curvatures
Response R
det − k·tr²
Edges
One λ large
Harris corner detector
Harris analyzes local image derivatives summed in a window: both eigenvalues large → corner; one large → edge; both small → flat region.
Response trick
The Harris response avoids explicit eigen decomposition using determinant and trace of M.
Interpretation
det & trace
det(M)=λ1λ2, trace(M)=λ1+λ2—combined into R with empirical k ≈ 0.04–0.06.
Shi-Tomasi
Uses min(λ1,λ2) as corner score—often more stable for tracking.
Window size
Larger windows smooth noise but reduce localization; Gaussian weighting is common.
Not scale-invariant
Classic Harris is not intrinsically scale invariant—multi-scale or blob detectors address that.
Eigenvalue diagram
Corner: both λ high · Edge: one λ high · Flat: both λ low