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Find corner points in image

corner is not recommended. Use detectHarrisFeatures or detectMinEigenFeatures in Computer Vision System Toolbox™ instead.


C = corner(I)
C = corner(I,method)
C = corner(I,N)
C = corner(I,method,N)
C = corner(___,Name,Value)


C = corner(I) detects corners in image I and returns their coordinates in matrix C.

C = corner(I,method) detects corners in image I using the specified method.

C = corner(I,N) detects corners in image I and returns a maximum of N corners.

C = corner(I,method,N) detects corners using the specified method and maximum number of corners.


C = corner(___,Name,Value) specifies parameters and corresponding values that control various aspects of the corner detection algorithm.


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I = checkerboard(50,2,2);
C = corner(I);
hold on

Input Arguments

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Grayscale or binary image, specified as an m-by-n numeric matrix.

Corner detection method, specified as 'Harris' for the Harris corner detector, or 'MinimumEigenvalue' for Shi & Tomasi's minimum eigenvalue method.

Maximum number of corners that the corner function can return, specified as a positive integer.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: corner(I,'QualityLevel',0.2) specifies the minimum quality level of corners in image I as 0.2.

Filter coefficients for the separable smoothing filter, specified as the comma-separated pair consisting of 'FilterCoefficients' and a numeric vector. The vector, V, must have odd length and a minimum length of 3. The outer product, V*V', gives the full filter kernel. The default filter coefficients are given by fspecial('gaussian',[5 1],1.5).

Minimum accepted quality of corners, specified as the comma-separated pair consisting of 'QualityLevel' and a numeric scalar in the range (0, 1). For a quality level Q, the toolbox rejects candidate corners with corner metric values less than Q * max(corner metric). Use larger values of Q to remove erroneous corners.

Sensitivity factor used in the Harris detection algorithm, specified as the comma-separated pair consisting of 'SensitivityFactor' and a numeric scalar in the range (0, 0.25). The smaller the sensitivity factor, the more likely the algorithm is to detect sharp corners. Use this parameter with the 'Harris' method only.

Output Arguments

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x and y coordinates of the corner points detected in image I, returned as a p-by-2 matrix.

Data Types: double


The corner and cornermetric functions both detect corners in images. For most applications, use the streamlined corner function to find corners in one step. If you want greater control over corner selection, use the cornermetric function to compute a corner metric matrix and then write your own algorithm to find peak values.


The corner function performs nonmaxima suppression on candidate corners, and corners are at least two pixels apart.

Introduced in R2010b