title: Detection of point landmarks in multidimensional tensor data.
abstract: This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor data are presented.
authors.affiliation: Dep. Señales y Comunicaciones, Univ. Las Palmas de Gran Canaria, Spain, Campus de Ta ra, s/n, 35017 Las Palmas, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Ruiz-Alzola
authors.affiliation: Department of Radiology, Harvard Medical School and Brigham & Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
authors.forename: R
authors.initials: R
authors.lastname: Kikinis
authors.affiliation: Department of Radiology, Harvard Medical School and Brigham & Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
authors.forename: C-F
authors.initials: CF
authors.lastname: Westin
keywords: Corner; Correlation; Gradient; Point landmark; Tensor data