Name
Abstract | ||
|---|---|---|
Manifold Pursuit (MP) extends Principal Component Analysis to be invariant to a desired group of image-plane transformations of an ensemble of un-aligned images.We derive a simple technique for projecting a misaligned target image onto the linear subspace defined by the superpositions of a collection of model images. We show that it is possible to generate a fixed projection matrix which would separate the projected image into the aligned projected target and a residual image which accounts for the misalignment. An iterative procedure is then introduced for eliminating the residual image and leaving the correctaligned projected target image.Taken together, we demonstrate a simple and effective technique for obtaining invariance to image-plane transformations within a linear dimensionality reduction approach. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICPR.2002.1048008 | Pattern Recognition, 2002. Proceedings. 16th International Conference |
Keywords | Field | DocType |
eigenvalues and eigenfunctions,image coding,image recognition,iterative methods,matrix algebra,principal component analysis,aligned projected target,appearance based recognition,fixed projection matrix,image-plane transformations,iterative procedure,linear dimensionality reduction approach,linear subspace,manifold pursuit,misaligned target image,model images,principal component analysis,projected image,residual image,unaligned images | Computer vision,Dimensionality reduction,Feature detection (computer vision),Pattern recognition,Iterative method,Computer science,Image texture,Binary image,Projection (linear algebra),Linear subspace,Invariant (mathematics),Artificial intelligence | Conference |
Volume | ISSN | ISBN |
3 | 1051-4651 | 0-7695-1695-X |
Citations | PageRank | References |
31 | 2.75 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amnon Shashua | 1 | 3396 | 384.93 |
| Anat Levin | 2 | 3578 | 212.90 |
| Shai Avidan | 3 | 3291 | 208.12 |