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2010

Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing

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Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing
In this paper, we address the tasks of detecting, segmenting, parsing, and matching deformable objects. We use a novel probabilistic object model that we call a hierarchical deformable template (HDT). The HDT represents the object by state variables defined over a hierarchy (with typically 5 levels). The hierarchy is built recursively by composing elementary structures to form more complex structures. A probability distribution – a parameterized exponential model – is defined over the hierarchy to quantify the variability in shape and appearance of the object at multiple scales. To perform inference – to estimate the most probable states of the hierarchy for an input image – we use a bottom-up algorithm called compositional inference. This algorithm is an approximate version of dynamic programming where approximations are made (e.g., pruning) to ensure that the algorithm is fast while maintaining high performance. We adapt the structure-perceptron algorithm to estimate the p...
Long Zhu, Yuanhao Chen, Alan L. Yuille
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PAMI
Authors Long Zhu, Yuanhao Chen, Alan L. Yuille
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