Exploiting Part-Based Models and Edge Boundaries for Object Detection

This paper explores how to exploit shape information to perform object class recognition. We use a sparse partbased model to describe object categories defined by shape. The sparseness allows the relative spatial relationship between parts to be described simply. It is possible, with this model, to highlight potential locations of the object and its parts in novel images. Subsequently these areas are examined by a more flexible shape model that measures if the image data provides evidence of the existence of boundary/connecting curves between connected hypothesized parts. From these measurements it is possible to construct a very simple cost function which indicates the presence or absence of the object class. The part-based model is designed to decouple variations due to affine warps and other forms of shape deformations. The latter are modeled probabilistically using conditional probability distributions which describe the linear dependencies between the location of a part and a sub...

• Type: Conference
• Year: 2008
• Where: DICTA
• Authors: Josephine Sullivan, Oscar M. Danielsson, Stefan Carlsson
• Keywords: Applied Computing, DICTA 2008, Flexible Shape Model, Object Class, Object Class Recognition