A layered representation for model-based filtering and recognition

Describes an image representation, which is built over an edge map. The edges are grouped into straight line segments and properties of these segments are embedded sparsely in a three dimensional space. Specifically, the space is divided into layers, and segments associated with different (quantized) orientations are placed in different layers. Therefore we refer to this representation as a layered representation. This representation induces an implicit correspondence relation between line segments associated with close views of the same object. An induced image-based representation of objects is a collection of image representations corresponding to their different views. Standard subspace-based methods are used to approximate this collection with the Karhunen-Loeve (principal components) technique. Projection of the representation of a new, unfamiliar image onto this subspace is used for recognition and model-based filtering. In contrast to grey-level based subspace methods, this method is highly insensitive to clutter and occlusion