Name
Abstract | ||
|---|---|---|
Reconstructing geometric models of relief carvings are of great importance in preserving cultural heritages digitally. In case of reliefs, using laser scanners and structured lighting techniques is not always feasible or are very expensive given the uncontrolled environment. Single image shape from shading is an under-constrained problem that tries to solve for the surface normals given the intensity image. Various constraints are used to make the problem tractable. To avoid the uncontrolled lighting, we use a pair of images with and without the flash and compute an image under a known illumination. This image is used as an input to the shape reconstruction algorithms. We present techniques that try to reconstruct the shape from relief images using the prior information learned from examples. We learn the variations in geometric shape corresponding to image appearances under different lighting conditions using sparse representations. Given a new image, we estimate the most appropriate shape that will result in the given appearance under the specified lighting conditions. We integrate the prior with the normals computed from reflectance equation in a MAP framework. We test our approach on relief images and compare them with the state-of-the-art shape from shading algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ACPR.2013.61 | Pattern Recognition |
Keywords | Field | DocType |
single relief image,image appearance,state-of-the-art shape,relief image,different lighting condition,shape reconstruction algorithm,new image,shape reconstruction,geometric shape,intensity image,appropriate shape,single image shape,history,lighting,image reconstruction | Iterative reconstruction,Computer vision,Computer graphics (images),Computer science,Neural coding,Image representation,Image-based lighting,Artificial intelligence,Geometric shape,Reflectivity,Shape reconstruction,Photometric stereo | Conference |
Citations | PageRank | References |
1 | 0.37 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harshit Agrawal | 1 | 44 | 8.53 |
| Anoop M. Namboodiri | 2 | 255 | 26.36 |