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Scale Normalized Radial Fourier Transform as a Robust Image Descriptor

Evanthia Mavridou 1, * Manh-Dung Hoang 1 James L. Crowley 2 Augustin Lux 1
* Corresponding author
1 PRIMA - Perception, recognition and integration for observation of activity
CNRS - Centre National de la Recherche Scientifique : UMR5217, INPG - Institut National Polytechnique de Grenoble , UJF - Université Joseph Fourier - Grenoble 1, Inria Grenoble - Rhône-Alpes
Abstract : We present a new visual descriptor that combines a multi-scale Laplacian Profile with a Radial Discrete Fourier Transform. This descriptor exists at every position and scale in an image and provides a local feature vector that is both discriminant and robust to changes in orientation and scale. It has a variable description length, and thus can be easily adapted for a variety of applications, ranging from simple detection tasks on low power computing platforms to complex tasks requiring highly discriminant detectors. To demonstrate the discriminant power of this descriptor we employ it in its most compact form to construct a cascade of linear classifiers for detecting people in images. We compare this detector to cascades classifiers constructed using Haar wavelets, Gaussian derivatives and variable size block HOG descriptors. Our experiments show that a cascade with this descriptor performs well against the other three detectors when tested using a common publicly available data set. We examine the stability of the descriptor to changes in image rotation and scaling for different description lengths.
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Submitted on : Thursday, September 18, 2014 - 10:39:00 AM
Last modification on : Thursday, January 20, 2022 - 5:27:11 PM
Long-term archiving on: : Friday, December 19, 2014 - 12:26:23 PM


  • HAL Id : hal-01065463, version 1



Evanthia Mavridou, Manh-Dung Hoang, James L. Crowley, Augustin Lux. Scale Normalized Radial Fourier Transform as a Robust Image Descriptor. ICPR 2014, 22nd International Conference on Pattern Recognition, Aug 2014, Stockholm, Sweden. ⟨hal-01065463⟩



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