In this paper, we review most major filtering approaches to texture feature extraction and perform a comparative study. Filtering approaches included are Laws masks, ring/wedge filters, dyadic Gabor filter banks, wavelet transforms, wavelet packets and wavelet frames, quadrature mirror filters, discrete cosine transform, eigenfilters, optimized Gabor filters, linear predictors, and optimized finite impulse response filters. The features are computed as the local energy of the filter responses. The effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches. For reference, comparisons with two classical nonfiltering approaches, co-occurrence (statistical) and autoregressive (model based) features, are given. We present a ranking of the tested approaches based on extensive experiments.

The design of filters for texture feature extraction is addressed. Based on a new feature extraction model, optimization approaches utilizing various feature (energy) separation criteria are developed. Both two- and multiple-texture problems are addressed. The approaches are assessed by supervised segmentation experiments. The experiments also include results from alternative filter optimization approaches.

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A novel fault extraction procedure for 3D seismic is presented. The procedure consists of three steps. The first step enhances the spatial discontinuities in the seismic data (fault attribute generation). The second step significantly improves the fault attributes by suppressing noise and remains of non-faulting events. This is achieved by the cooperative behavior of thousands of "artificial ants". The advantages with this method will be demonstrated. In particular, the faults can be split into different nonintersecting sub-systems. These sub-systems ease the extraction of fault surfaces in the third step of the process.

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