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In this work we describe a fast and stable algorithm for the computation ofthe orthogonal moments of an image. Indeed, orthogonal moments arecharacterized by a high discriminative power, but some of their possibleformulations are characterized by a large computational complexity, whichlimits their real-time application. This paper describes in detail an approachbased on recurrence relations, and proposes an optimized Matlab implementationof the corresponding computational procedure, aiming to solve the abovelimitations and put at the community's disposal an efficient and easy to usesoftware. In our experiments we evaluate the effectiveness of the recurrenceformulation, as well as its performance for the reconstruction task, incomparison to the closed form representation, often used in the literature. Theresults show a sensible reduction in the computational complexity, togetherwith a greater accuracy in reconstruction. In order to assess and compare theaccuracy of the computed moments in texture analysis, we perform classificationexperiments on six well-known databases of texture images. Again, therecurrence formulation performs better in classification than the closed formrepresentation. More importantly, if computed from the GLCM of the image usingthe proposed stable procedure, the orthogonal moments outperform in somesituations some of the most diffused state-of-the-art descriptors for textureclassification.

Fast and accurate computation of orthogonal moments for texture analysis