Autor(es):
Farrajota, Miguel 
; Martins, J. C. ; Rodrigues, J. M. F. ; du Buf, J. M. H.
Data: 2011
Identificador Persistente: http://hdl.handle.net/10400.1/2097
Origem: Sapientia - Universidade do Algarve
Assunto(s): Visão humana
Descrição
A biological disparity energy model can estimate local depth information
by using a population of V1 complex cells. Instead of applying an analytical
model which explicitly involves cell parameters like spatial frequency,
orientation, binocular phase and position difference, we developed a model
which only involves the cells’ responses, such that disparity can be extracted
from a population code, using only a set of previously trained cells
with random-dot stereograms of uniform disparity. Despite good results
in smooth regions, the model needs complementary processing, notably at
depth transitions. We therefore introduce a new model to extract disparity
at keypoints such as edge junctions, line endings and points with large
curvature. Responses of end-stopped cells serve to detect keypoints, and
those of simple cells are used to detect orientations of their underlying
line and edge structures. Annotated keypoints are then used in the leftright
matching process, with a hierarchical, multi-scale tree structure and
a saliency map to segregate disparity. By combining both models we can
(re)define depth transitions and regions where the disparity energy model
is less accurate.
