Scale Normalized Radial Fourier Transform as a Robust Image Descriptor

Autor: James L. Crowley, Evanthia Mavridou, Manh-Dung Hoang, Augustin Lux
Přispěvatelé: Perception, recognition and integration for observation of activity (PRIMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Zdroj: ICPR 2014, 22nd International Conference on Pattern Recognition
ICPR 2014, 22nd International Conference on Pattern Recognition, Aug 2014, Stockholm, Sweden
ICPR
Popis: International audience; 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.
Databáze: OpenAIRE