Recognizing corners by fitting parametric models
International Journal of Computer Vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Spatio-Temporal Image Processing: Theory and Scientific Applications
Spatio-Temporal Image Processing: Theory and Scientific Applications
Wavelet-based corner detection technique using optimal scale
Pattern Recognition Letters
Wavelet-based corner detection using eigenvectors of covariance matrices
Pattern Recognition Letters
Steerable wedge filters for local orientation analysis
IEEE Transactions on Image Processing
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
Multiscale corner detection by using wavelet transform
IEEE Transactions on Image Processing
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Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator.