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Scale-invariant medial features based on gradient vector flow fields

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83902

Engel,  D
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83871

Curio,  C
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Engel, D., & Curio, C. (2008). Scale-invariant medial features based on gradient vector flow fields. In 19th International Conference on Pattern Recognition (ICPR 2008) (pp. 1-4). Piscataway, NJ, USA: IEEE Service Center.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C63B-4
Zusammenfassung
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.