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ICPR
2008
IEEE

Scale-invariant medial features based on gradient vector flow fields

12 years 28 days ago
Scale-invariant medial features based on gradient vector flow fields
We propose a novel set of medial feature interest points based on gradient vector flow (GVF) fields [18]. We exploit the long ranging GVF fields for symmetry estimation by calculating the flux flow on it. We propose interest points that are located on maxima of that flux flow and offer a straight forward way to estimate salient local scales. The features owe their robustness in clutter to the nature of the GVF which accomplishes two goals simultaneously - smoothing of orientation information and its preservation at salient edge boundaries. A learning framework based on them, in contrast to classical edge-based feature detectors, would unlikely be distracted by background clutter and spurious edges, as these new mid-level features are shape-centered. We evaluate our scale-invariant feature coding scheme against standard SIFT keypoints by demonstrating generalization over scale in a patch-based pedestrian detection task.
Cristóbal Curio, David Engel
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Cristóbal Curio, David Engel
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