BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)

Local Linear Transforms for Texture Measurements

M. Unser

Signal Processing, vol. 11, no. 1, pp. 61–79, July 1986.

The Nth order probability density function for pixels in a restricted neighborhood may be characterized by a set of N histograms (or some corresponding moments) computed along appropriately chosen axes. The projections on those axes are obtained from a local linear transform of the local neighborhood vector. This approach is closely related to filter bank analysis methods and gives a statistical justification for the extraction of texture properties by means of convolution operators or local matches. Optimal and sub-optimal linear operators are derived for texture analysis and classification. Experimental results indicate that the method is robust, flexible, and that it performs as well as standard co-occurrence based methods for texture classification. The proposed approach enables texture characterization with a lower number of features and it is also computationally more appealing. 

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AUTHOR="Unser, M.",
TITLE="Local Linear Transforms for Texture Measurements",
JOURNAL="Signal Processing",
YEAR="1986",
volume="11",
number="1",
pages="61--79",
month="July",
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