Steerable Filters

This paper presents the fusion of monogenic signal process- ing and dierential geometry to enable monogenic analyzing of local intrinsic 2D features of low level image data. New rotational invariant features such like structure and geometry (angle of intersection) of two superimposed intrinsic 1D signals will be extracted without the need of any steerable filters. These features are important for

This paper proposes a new invariant corner detection algorithm using steerable filters and Harris corner detection. The steerable filters have better orientation selectivity and multi-orientation image decomposition that provide a useful front-end for image-processing and computer vision applications. Corners, in image analysis, are important features for image registration, stereo matching, motion tracking and object recognition. These corners are referred as interest points or key points for image registration. In this paper, we compare the performance of SUSAN, Harris, and propose corner detectors in terms of consistency.

Airborne gravity surveys are becoming increasingly more popular in the mining and the oil industry for exploration and geological mapping. Usually in airborne gravity surveys magnetic data are collected as well. Although the magnetic data acquired during gravity surveys generally have lower spectral and spatial resolution than standard high-resolution aeromagnetic (HRAM) data, they add significant value to the interpretation of the acquired gravity data. In many cases, the gravity and magnetic data are responding to the same geological source. For example, structural discontinuities such as faults, fractures and geological contacts can create lateral contrasts in density and magnetization of rocks. These contrasts often generate gravity and magnetic signatures. However, due to density and magnetic variations of the rocks these structural discontinuities are partly detected on the gravity image and partly on the magnetic image. Therefore, we think that if we combine the two images to...

IRON is a low level operator dedicated to the estimation of single and multiple local orientations in images. Previous works have shown that IRON is more accurate and more selective than Gabor and Steerable filters, for textures corrupted with Gaussian noise. In this paper, we propose two new features. The first one is dedicated to the estimation of orientation in images damaged by impulsive noise. The second one applies when images are corrupted with an amplitude modulation, such as an inhomogeneous lighting.