Tom Dela Haije

Dr. Tom Dela Haije


Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 November 2013
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Book Chapter
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AFAndrea Fuster
Andrea Fuster
ATAntonio Tristan-Vega
Antonio Tristan-Vega
THTom Dela Haije
Tom Dela Haije
CWCarl-Fredrik Westin
Carl-Fredrik Westin
LFLuc Florack
Luc Florack

17 April 2014
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Book Chapter
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THTom Dela Haije
Tom Dela Haije
RDRemco Duits
Remco Duits
CTChantal M. W. Tax
Chantal M. W. Tax

1 January 2015
Verified
Book Chapter
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LFLuc Florack
Luc Florack
THTom Dela Haije
Tom Dela Haije
AFAndrea Fuster
Andrea Fuster

14 November 2014 in Axioms
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Journal Article
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LALaura Astolas
Laura Astolas
NSNeda Sepasian
Neda Sepasian
THTom Dela Haije
Tom Dela Haije
AFAndrea Fuster
Andrea Fuster
LFLuc Florack
Luc Florack
In Riemannian geometry, a distance function is determined by an inner product on the tangent space. In Riemann–Finsler geometry, this distance function can be determined by a norm. This gives more freedom on the form of the so-called indicatrix or the set of unit vectors. This has some interesting applications, e.g., in medical image analysis, especially in diffusion weighted imaging (DWI). An important application of DWI is in the inference of the local architecture of the tissue, typically consisting of thin elongated structures, such as axons or muscle fibers, by measuring the constrained diffusion of water within the tissue. From high angular resolution diffusion imaging (HARDI) data, one can estimate the diffusion orientation distribution function (dODF), which indicates the relative diffusivity in all directions and can be represented by a spherical polynomial. We express this dODF as an equivalent spherical monomial (higher order tensor) to directly generalize the (second order) diffusion tensor approach. To enable efficient computation of Riemann–Finslerian quantities on diffusion weighted (DW)-images, such as the metric/norm tensor, we present a simple and efficient algorithm to invert even order spherical monomials, which extends the familiar inversion of diffusion tensors, i.e., symmetric matrices.

9 April 2016
Verified
Book Chapter
Closed access
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THTom Dela Haije
Tom Dela Haije
NSNeda Sepasian
Neda Sepasian
AFAndrea Fuster
Andrea Fuster
LFLuc Florack
Luc Florack

8 April 2016 in arXiv
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Preprint
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RDRemco Duits
Remco Duits
AGArpan Ghosh
Arpan Ghosh
THTom Dela Haije
Tom Dela Haije
Alexey MashtakovAlexey Mashtakov
Alexey Mashtakov
We consider the problem $\mathbf{P_{curve}}$ of minimizing $\int \limits_0^L \sqrt{\xi^2 + \kappa^2(s)} \, {\rm d}s$ for a curve $\mathbf{x}$ on $\mathbb R$ with fixed boundary points and directions. Here the total length $L\geq 0$ is free, $s$ denotes the arclength parameter, $\kappa$ denotes the absolute curvature of $\mathbf{x}$, and $\xi>0$ is constant. We lift problem $\mathbf{P_{curve}}$ on $\mathbb R^3$ to a sub-Riemannian problem $\mathbf{P_{mec}}$ on $\operatorname{SE(3)}\nolimits/(\{\mathbf{0}\}\times \operatorname{SO(2)}\nolimits)$. Here, for admissible boundary conditions, the spatial projections of sub-Riemannian geodesics do not exhibit cusps and they solve problem $\mathbf{P_{curve}}$. We apply the Pontryagin Maximum Principle (PMP) and prove Liouville integrability of the Hamiltonian system. We derive explicit analytic formulas for such sub-Riemannian geodesics, relying on the co-adjoint orbit structure, an underlying Cartan connection, and the matrix representation of $\operatorname{SE(3)}\nolimits$ arising in the Cartan-matrix. These formulas allow us to extract geometrical properties of the sub-Riemannian geodesics with cuspless projection, such as planarity conditions, explicit bounds on their torsion, and their symmetries. Furthermore, they allow us to parameterize all admissible boundary conditions reachable by geodesics with cuspless spatial projection. Such projections lay in the upper half space. We prove this for most cases, and the rest is checked numerically. Finally, we employ the formulas to numerically solve the boundary value problem, and visualize the set of admissible boundary conditions.