Indexing for local appearance-based recognition of planar objects

摘要

Local appearance-based approaches for planar object recognition do not support efficient for indexing of object models. the size of the feature set which needs to be stored and processed is also large. To reduce database size, we propose and optimal feature extraction technique that selects only the salient features of an objects. Since in typical local appearance-based systems, the actual feature information is not isolated from the background, scene clutter causes error in recognition. We propose a mechanism whereby this shortcoming can be alleviated. Further, by indexing onto the space, we propose to improve the performance in terms of computation time and Suppression of false positives.