Illumination Invariant Recognition of Three-Dimensional Texture in Color Images

In this paper, illumination-affine invariant methods are presented based onaffine moment normalization techniques, Zernike moments, and multiband correlation functions. Themethods are suitable for the illumination invariant recognition of 3D color texture. Complex valuedmoments (i.e., Zernike moments) and affine moment normalization are used in the derivation ofillumination affine invariants where the real valued affine moment invariants fail to provide affineinvariants that are independent of illumination changes. Three different moment normalizationmethods have been used, two of which are based on affine moment normalization technique and thethird is based on reducing the affine transformation to a Euclidian transform. It is shown that fora change of illumination and orientation, the affinely normalized Zernike moment matrices arerelated by a linear transform. Experimental results are obtained in two tests: the first is usedwith textures of outdoor scenes while the second is performed on the well-known CUReT texturedatabase. Both tests show high recognition efficiency of the proposed recognition methods.

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining.Such tasks have become possible using ground-based,close-range hyperspectral sensors which can remotely measure the reflectance properties of the environ-ment with high spatial and spectral resolution.However,autonomous mapping of mineral spectra mea-sured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene.An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm.A pipeline for unsupervised mapping of spectra on a mine face is pro-posed which draws from several recent advances in the hyperspectral machine learning literature.The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data.The pipeline is eval-uated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale.The combined system is shown to produce a superior map to its constituent algo-rithms,and the consistency of its mapping capability is demonstrated using data acquired at two differ-ent times of day.