Technical Report CIS9708

Title: A Layered Representation for Model-based Filtering and Recognition
Authors: Milena Salman and Michael Lindenbaum
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Abstract: This work describes a new 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 to a layered representation. This representation induces an implicit correspondence relation between line segments associated with close views of the same object. Unlike grey level images, this representation satisfies the desirable local linearity/convexity property, meaning that the average of two representations of the same object, corresponding to two close views, is also an (approximate) representation of another view of the same object. An induced view-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 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 previous subspace methods working with grey-level images this method is highly insensitive to clutter and irrelevant illumination data.
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