| dc.creator |
Monaleche Cirstea, S |
|
| dc.creator |
Kung, SY |
|
| dc.creator |
McCormick, M |
|
| dc.creator |
Aggoun, A |
|
| dc.date |
2008-11-27T16:25:20Z |
|
| dc.date |
2008-11-27T16:25:20Z |
|
| dc.date |
2003 |
|
| dc.date.accessioned |
2022-05-25T13:26:21Z |
|
| dc.date.available |
2022-05-25T13:26:21Z |
|
| dc.identifier |
Journal of VLSI Signal Processing. 35(1): 5–18 |
|
| dc.identifier |
http://bura.brunel.ac.uk/handle/2438/2856 |
|
| dc.identifier |
http://www.springerlink.com/content/t885833m6kk56530/ |
|
| dc.identifier |
http://dx.doi.org/10.1023/A:1023386402756 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/165911 |
|
| dc.description |
The published version of this article is accessible from the link below. |
|
| dc.description |
The paper presents a novel algorithm for object space reconstruction from the planar (2D) recorded data set of a 3D-integral image. The integral imaging system is described and the associated point spread function is given. The space data extraction is formulated as an inverse problem, which proves ill-conditioned, and tackled by imposing additional conditions to the sought solution. An adaptive constrained 3D-reconstruction regularization algorithm based on the use of a sigmoid function is presented. A hierarchical multiresolution strategy which employes the adaptive constrained algorithm to obtain highly accurate intensity maps of the object space is described. The depth map of the object space is extracted from the intensity map using a weighted Durbin–Willshaw algorithm. Finally, illustrative simulation results are given. |
|
| dc.format |
893017 bytes |
|
| dc.format |
text/plain |
|
| dc.language |
en |
|
| dc.publisher |
Kluwer |
|
| dc.subject |
3D Imaging |
|
| dc.subject |
Inverse problems |
|
| dc.subject |
Object space reconstruction |
|
| dc.subject |
Regularisation methods |
|
| dc.title |
3D-object space reconstruction from planar recorded data |
|
| dc.type |
Research Paper |
|
| dc.coverage |
13 |
|