Ged McGunnigle

Classifying Surface Texture while Simultaneously Estimating Illumination Direction

We propose a novel classifier that both classifies surface texture and simultaneously estimates the unknown illumination conditions. A new formal model of the dependency of texture features on lighting direction is developed which shows that their mean vectors are trigonometric functions of the illuminations’ tilt and slant angles. This is used to develop a probabilistic description of feature behaviour which forms the basis of the new classifier. Given a feature set from an image of an unknown texture captured under unknown illumination conditions the algorithm first estimates the most likely illumination direction for each possible texture class. These estimates are used to calculate the class likelihoods and the classification is made accordingly.The ability of the classifier to estimate illuminant direction, and to assign the correct class, was tested on 55 real texture samples in two stages. The classifier was able to accurately estimate both the tilt and the slant angles of the light source for the majority of textures and gave a 98% classification rate.

The Effect of Illuminant Rotation on Texture Filters: Lissajous's Ellipses

Changes in the angle of illumination incident upon a 3D surface texture can significantly change its appearance. These changes can affect the output of texture features to such an extent that they cause complete misclassification. We present new theory and experimental results that show that changes in illumination tilt angle cause texture clusters to describe Lissajouss ellipses in feature space. We focus on texture features that may be modelled as a linear filter followed by an energy estimation process e.g. Laws filters, Gabor filters, ring and wedge filters. This general texture filter model is combined with a linear approximation of Lamberts cosine law to predict that the outputs of these filters are sinusoidal functions of illuminant tilt. Experimentation with 30 real textures verifies this proposal. Furthermore we use these results to show that the clusters of distinct textures describe different elliptical paths in feature space as illuminant tilt varies. These results have significant implications for illuminant tilt invariant texture classification.

Resolving handwriting from background printing using photometric stereo

Abstract We propose a scheme to resolve handwriting from background printing. The scheme detects the indentations made by the pen in the paper. Photometric stereo is used to recover the surface; a matched filter and classifier are used to detect the stroke indentation. We assess the effect of uniform and textured backgrounds on the recovery of the stroke and test the scheme on practical examples. The technique was found to work well with script written with a ballpoint pen and could effectively suppress even dark and strongly textured backgrounds. We conclude that this is a useful complement to existing techniques for background removal and is especially useful when there is no template available.

Estimating Lighting Direction and Classifying Textures

The appearance of a rough surface is affected by the direction from which it is lit and texture classifiers should account for this. We propose a classifier that is robust to lighting direction—even when the direction is unknown. An existing model of the dependency of texture features on lighting direction is used to develop a probabilistic model. Given a feature set, the algorithm estimates the most likely illumination direction for each texture class. The likelihoods of each candidate (with their estimated lighting) are compared to classify the sample. The ability of the classifier to identify illuminant direction, and to assign the correct class, was tested on 25 real texture samples. The classifier was able to accurately estimate both the azimuth and the zenith of the light source for most textures and gave a 98% classification rate.

Evaluating Kube and Pentland's fractal imaging model

The paper assesses the validity of a model, proposed by Kube and Pentland (1988), that relates a rough surface to its image texture. Simulation was used to assess whether a linear approximation is appropriate, and whether the optimal linear filter agrees with the predictions of Kube and Pentlands model. The predictions of the model about the image directionality were also assessed on real images. It was found that a linear model is capable of modeling the imaging process for surfaces of moderate roughness and Lambertian reflectance, and that, subject to a small modification, Kube and Pentlands model accurately predicts the relationship between surface and image spectra.

Segmentation of Rough Surfaces using Reflectance

The segmentation of rough surfaces using their reflectance properties is considered. We present a technique to estimate the orientation of surface facets whose reflectance functions are unknown. The reflectance characteristics of each facet are estimated individually allowing this technique to be applied to non-homogeneous surfaces. Non-Lambertian components are attenuated allowing shape estimation with classical photometric stereo. Simulations with rough surfaces rendered with Phong’ s model indicate that this approach extends the range of reflectance functions to which classical photometric stereo can be applied. The recovered surface derivatives, together with the original intensity images are used to construct reflectance maps. These are used as features for segmentation. A reflectance based classifier is found to be more accurate than an intensity classifier .

On the use of gradient space eigenvalues for rotation invariant texture classification

Many image-rotation invariant texture classification approaches have been presented previously. This paper proposes a novel surface-rotation invariant scheme. It uses the eigenvalues of a surfaces gradient-space distribution as its features. Unlike the partial derivatives, from which they are computed, these eigenvalue features are invariant to surface rotation. First, we show that a simple classifier using a single isotropic feature (grey-level standard deviation) is not invariant to surface rotation. Then a practical surface rotation invariant classifier that uses photometric stereo to estimate surface derivatives is developed. Results for both classifiers are presented.

A Comparison of Three Rough Surface Classifiers

In this papertextureanalysistechniquesareusedto segmentroughsurfacesinto regionsof homogeneous texture. Theperformanceof threerough surfaceclassifierswasassessed andcompared.Theclassifiersdiffer in their discriminationaswell astheir inputandcomputational requirements. Experimentswereusedto identify thefailuremodesof theclassifiersandto identify which classifieris bestsuitedto a particulartask. A seriesof guidelinesfor thechoiceof classifierarepresentedandjustified.

The response of texture features to illuminant rotation

Rotation of the illuminant source about a subject textured surface can cause catastrophic failure of texture classification schemes. This is due to the variation of texture feature output that can occur when the illuminant direction is varied. This paper uses theory and experiment to show that the outputs of linear texture filters, and their features, are sinusoidal functions of the illuminant tilt angle.