Image Descriptors Based on Curvature Histograms View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014-10-15

AUTHORS

Philipp Fischer , Thomas Brox

ABSTRACT

Descriptors based on orientation histograms are widely used in computer vision. The spatial pooling involved in these representations provides important invariance properties, yet it is also responsible for the loss of important details. In this paper, we suggest a way to preserve the details described by the local curvature. We propose a descriptor that comprises the direction and magnitude of curvature and naturally expands classical orientation histograms like SIFT and HOG. We demonstrate the general benefit of the expansion exemplarily for image classification, object detection, and descriptor matching. More... »

PAGES

239-249

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-11752-2_19

DOI

http://dx.doi.org/10.1007/978-3-319-11752-2_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046901468


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