Scale Normalized Radial Fourier Transform as a Robust Image Descriptor

International audienceWe 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

Multiscale Shape Description with Laplacian Profile and Fourier Transform

International audienceWe propose a new local multiscale image descriptor of vari-able size. The descriptor combines Laplacian of Gaussian values at dif-ferent scales with a Radial Fourier Transform. This descriptor provides a compact description of the appearance of a local neighborhood in a manner that is robust to changes in scale and orientation. We evaluate this descriptor by measuring repeatability and recall against 1-precision with the Affine Covariant Features benchmark dataset and as well as with a set of textureless images from the MIRFLICKR Retrieval Evalu-ation dataset. Experiments reveal performance competitive to the state of the art, while providing a more compact representation