Deformable energy-minimizing contours are used in image analysis, particularly to detect boundaries from objects. From the Mumford-Shah model we derive an equation to move such contours towards the boundary, which minimizes the functional. The external force is computed using the mean value of the inside and outside area of the contour. Various ima...
This dissertation deals with customised image analysis tools in cancer research. In the field of biomedical sciences, mathematical imaging has become crucial in order to account for advancements in technical equipment and data storage by sound mathematical methods that can process and analyse imaging data in an automated way. This thesis contribute...
Nous montrons comment utiliser la stéréophotométrie colorée pour améliorer le relief fourni par un capteur RVB-D. Le capteur est équipé de trois LEDs colorées, de telle sorte que l'image RVB permet de retrouver les détails les plus fins du relief, grâce à la stéréophotométrie. Cette estimation fine du relief est fusionnée avec la carte de profondeu...
Published in Advanced Nonlinear Studies
In this paper we study the existence of weak positive solutions for the following class of quasilinear Schrödinger equations −Δu + V(x)u − [Δ(u2)]u = h(u) in ℝN, where h satisfies some “mountain-pass” type assumptions and V is a nonnegative continuous function. We are interested specially in the case where the potential V is neither bounded away fr...
Published in Advanced Nonlinear Studies
In this work we establish existence and multiplicity of solutions for resonant-superlinear elliptic problems using appropriate variational methods. The nonlinearity is resonant at −∞ and superlinear at +∞ and the resonance phenomena occurs precisely in the first eigenvalue of the corresponding linear problem. Our main theorems are stated without th...
The work presented in this dissertation is motivated by classical problems in signal and image processing from the perspective of variational and PDE-based methods. Analytically encoding qualitative features of signals into variational energies in conjunction with modern methods in sparse optimization allows for well-founded and robust models, the ...
The work presented in this dissertation is motivated by classical problems in signal and image processing from the perspective of variational and PDE-based methods. Analytically encoding qualitative features of signals into variational energies in conjunction with modern methods in sparse optimization allows for well-founded and robust models, the ...
The work presented in this dissertation is motivated by classical problems in signal and image processing from the perspective of variational and PDE-based methods. Analytically encoding qualitative features of signals into variational energies in conjunction with modern methods in sparse optimization allows for well-founded and robust models, the ...
The work presented in this dissertation is motivated by classical problems in signal and image processing from the perspective of variational and PDE-based methods. Analytically encoding qualitative features of signals into variational energies in conjunction with modern methods in sparse optimization allows for well-founded and robust models, the ...
The work presented in this dissertation is motivated by classical problems in signal and image processing from the perspective of variational and PDE-based methods. Analytically encoding qualitative features of signals into variational energies in conjunction with modern methods in sparse optimization allows for well-founded and robust models, the ...
