Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Synthesis and characterisation of metal oxyhydroxide and oxide nanomaterials

In this work, a range of nanomaterials have been synthesised based on metal oxyhydroxides MO(OH), where M=Al, Co, Cr, etc. Through a self-assembly hydrothermal route, metal oxyhydroxide nanomaterials with various morphologies were successfully synthesised: one dimensional boehmite (AlO(OH)) nanofibres, zero dimensional indium hydroxide (In(OH)3) nanocubes and chromium oxyhydroxide (CrO(OH)) nanoparticles, as well as two dimensional cobalt hydroxide and oxyhydroxide (Co(OH)2 & CoO(OH)) nanodiscs. In order to control the synthetic nanomaterial morphology and growth, several factors were investigated including cation concentration, temperature, hydrothermal treatment time, and pH. Metal ion doping is a promising technique to modify and control the properties of materials by intentionally introducing impurities or defects into the material. Chromium was successfully applied as a dopant for fabricating doped boehmite nanofibres. The thermal stability of the boehmite nanofibres was enhanced by chromium doping, and the photoluminescence property was introduced to the chromium doped alumina nanofibres. Doping proved to be an efficient method to modify and functionalize nanomaterials. The synthesised nanomaterials were fully characterised by X-ray diffraction (XRD), transmission electron microscopy (TEM) combined with selected area electron diffraction (SAED), scanning electron microscopy (SEM), BET specific surface area analysis, X-ray photoelectron spectroscopy (XPS) and thermo gravimetric analysis (TGA). Hot-stage Raman and infrared emission spectroscopy were applied to study the chemical reactions during dehydration and dehydroxylation. The advantage of these techniques is that the changes in molecular structure can be followed in situ and at the elevated temperatures.