Common-Frame Model for Object Recognition

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Common-Frame Model for Object Recognition
A generative probabilistic model for objects in images is presented. An object consists of a constellation of features. Feature appearance and pose are modeled probabilistically. Scene images are generated by drawing a set of objects from a given database, with random clutter sprinkled on the remaining image surface. Occlusion is allowed. We study the case where features from the same object share a common reference frame. Moreover, parameters for shape and appearance densities are shared across features. This is to be contrasted with previous work on probabilistic `constellation' models where features depend on each other, and each feature and model have different pose and appearance statistics [1, 2]. These two differences allow us to build models containing hundreds of features, as well as to train each model from a single example. Our model may also be thought of as a probabilistic revisitation of Lowe's model [3, 4]. We propose an efficient entropy-minimization inferenc...
Pierre Moreels, Pietro Perona
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Pierre Moreels, Pietro Perona
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