We propose a novel monocular vision-based framework for both satellite recognition and pose estimation, using homeomorphic manifold analysis. We use a unified conceptual manifold to represent continuous pose variation of all satellites in the visual input space, learn nonlinear function mapping from conceptual manifold representation to visual inputs, and decompose discrete category variation in the mapping coefficient space. Experimental results on a simulated image data set show the effectiveness and robustness of our approach.
|Original language||English (US)|
|Number of pages||8|
|Journal||IEEE Transactions on Aerospace and Electronic Systems|
|State||Published - Jan 1 2015|
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Electrical and Electronic Engineering