The Brodatz Album has become the de facto standard for evaluating texture algorithms, with hundreds of studies having been applied to small sets of its images. This paper compares two powerful recognition algorithms, principal components analysis and multiscale autoregressive models, by evaluating them on a 999-image database derived from the entire Brodatz Album. The variety of homogeneous and non-homogeneous images studied is thus nearly an order of magnitude larger than has been compared before, giving one snapshot of the ``state of the art'' in real-time texture recognition.
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