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      SHAPE AND TEXTURE INDEXES APPLICATION TO CELL NUCLEI CLASSIFICATION

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          Image analysis via the general theory of moments*

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            Texture classification and segmentation using wavelet frames.

             M. Unser (1995)
            This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform. The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l(2) and that it has a fast iterative algorithm. A texture is characterized by a set of channel variances estimated at the output of the corresponding filter bank. Classification experiments with l(2) Brodatz textures indicate that the discrete wavelet frame (DWF) approach is superior to a standard (critically sampled) wavelet transform feature extraction. These results also suggest that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, and the like). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is also provided. Finally, the DWF feature extraction technique is incorporated into a simple multicomponent texture segmentation algorithm, and some illustrative examples are presented.
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              Shape Quantization and Recognition with Randomized Trees

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                Author and article information

                Journal
                International Journal of Pattern Recognition and Artificial Intelligence
                Int. J. Patt. Recogn. Artif. Intell.
                World Scientific Pub Co Pte Lt
                0218-0014
                1793-6381
                February 2013
                February 2013
                : 27
                : 01
                : 1357002
                Article
                10.1142/S0218001413570024
                © 2013

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