\( \newcommand{\cat}[1] {\mathrm{#1}} \newcommand{\catobj}[1] {\operatorname{Obj}(\mathrm{#1})} \newcommand{\cathom}[1] {\operatorname{Hom}_{\cat{#1}}} \newcommand{\multiBetaReduction}[0] {\twoheadrightarrow_{\beta}} \newcommand{\betaReduction}[0] {\rightarrow_{\beta}} \newcommand{\betaEq}[0] {=_{\beta}} \newcommand{\string}[1] {\texttt{"}\mathtt{#1}\texttt{"}} \newcommand{\symbolq}[1] {\texttt{`}\mathtt{#1}\texttt{'}} \newcommand{\groupMul}[1] { \cdot_{\small{#1}}} \newcommand{\groupAdd}[1] { +_{\small{#1}}} \newcommand{\inv}[1] {#1^{-1} } \newcommand{\bm}[1] { \boldsymbol{#1} } \require{physics} \require{ams} \require{mathtools} \)

Harris corner detector

A Harris corner detector extracts corner features from images. It identifies windows of the image containing corners as those where a small shift in the window position in any direction causes large changes in the pixel intensities within the window. This works because:

• windows that have large intensity shifts but only for one direction: these contain [what?].
• windows that don't have large intensity shifts are flat regions of the image.
• windows that have large intensities in two directions contain [what?].

How many directions that lead to large intensity changes is determined by inspecting the [something] of the structure tensor of the image.

Algorithm

The proceedure for applying the Harris corner detector is as follows:

1. Convert the image to grayscale.
2. Optionally smooth the image with a Gaussian filter.
3. Compute the image gradients in the x and y directions, for example using the Sobel operator.
4. Calculate the Harris response.
5. Find points that have a high Harris response (above a threshold) and are local maxima in their neighborhood (e.g. within a 3x3 window). This is non-maximum suppression.