Computer Vision and Image Understanding
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
ALSBIR: A local-structure-based image retrieval
Pattern Recognition
A method for single-stimulus quality assessment of segmented video
EURASIP Journal on Applied Signal Processing
Robust fusion of irregularly sampled data using adaptive normalized convolution
EURASIP Journal on Applied Signal Processing
Computer Vision and Image Understanding
Deformable model with a complexity independent from image resolution
Computer Vision and Image Understanding
Estimation of curvature along curves with application to fibres in 3D images of paper
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Continuous orientation representation for arbitrary dimensions: a generalized knutsson mapping
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
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Local curvature represents an important shape parameter of space curves which are well described by differential geometry. We have developed an estimator for local curvature of space curves embedded in n-dimensional (n-D) grey-value images. There is neither a segmentation of the curve needed nor a parametric model assumed. Our estimator works on the orientation field of the space curve. This orientation field and a description of local structure is obtained by the gradient structure tensor. The orientation field has discontinuities; walking around a closed contour yields two such discontinuities in orientation. This field is mapped via the Knutsson (1985) mapping to a continuous representation; from a n-D vector to a symmetric n2-D tensor field. The curvature of a space curve, a coordinate invariant property, is computed in this tensor field representation. An extensive evaluation shows that our curvature estimation is unbiased even in the presence of noise, independent of the scale of the object and furthermore the relative error stays small.