|
Antonio Lopez, David Lloret, Joan Serrat, & Juan J. Villanueva. (2000). Multilocal Creaseness Based on the Level-Set Extrinsic Curvarture..
|
|
|
Ricardo Toledo, X. Orriols, X. Binefa, Petia Radeva, Jordi Vitria, & Juan J. Villanueva. (2000). Tracking Elongated Structures using Statistical Snakes..
|
|
|
Antonio Lopez. (1997). Ridge/Valley-like structures: Creases, separatrices and drainage patterns.
|
|
|
Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, J. Mauri, & Petia Radeva. (2006). Using Radio Frequency Reconstructed IVUS Images in Tissue Classification.
|
|
|
David Rotger, Petia Radeva, & Oriol Rodriguez. (2006). Vessel Tortuosity Extraction from IVUS Images.
|
|
|
J. Mauri, E Fernandez-Nofrerias, E. Esplugas, A. Cequier, David Rotger, Ricardo Toledo, et al. (2000). Ecografia Intracoronaria: Navegacion Informatica por el cubo de datos de las imagenes..
|
|
|
M. Gomez, J. Mauri, E. Fernandez-Nofrerias, Oriol Rodriguez-Leor, Carme Julia, Petia Radeva, et al. (2002). Nuevos Avances para la correlacion de imagenes angiograficas y de ecograia intracoronaria..
|
|
|
Antonio Lopez, Felipe Lumbreras, A. Martinez, Joan Serrat, Xavier Roca, Javier Varona, et al. (1997). Aplicaciones de la vision por computador a la industria..
|
|
|
Bhaskar Chakraborty. (2008). View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts.
|
|
|
Pierluigi Casale. (2008). Social Environment Description from Data Collected with a Wearable Device.
|
|
|
Francesco Ciompi. (2008). ECOC-based Plaque Classification using In-vivo and Exvivo Intravascular Ultrasound Data.
|
|
|
Marco Pedersoli. (2008). A Multiresolution Cascade for Human Detection.
|
|
|
Dani Rowe. (2008). Towards Robust Multiple-Target Tracking in Unconstrained Human-Populated Environments.
|
|
|
Carme Julia. (2008). Missig Data Matrix Factorization Addressing the Structure from Motion Problem.
|
|
|
Antonio Lopez, David Lloret, & Joan Serrat. (1998). Creaseness measures for CT and MR image registration..
Abstract: Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy
|
|