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 direc-tion, 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. 1

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.

In this paper texture analysis techniques are used to segment rough surfaces into regions of homogeneous texture. The performance of three rough surface classifiers was assessed and compared. The classifiers differ in their discrimination as well as their input and computational requirements. Experiments were used to identify the failure modes of the classifiers and to identify which classifier is best suited to a particular task. A series of guidelines for the choice of classifier are presented and justified. 1

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 Pentland's 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 Pentland's model accurately predicts the relationship between surface and image spectra.

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 surface's 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.

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.