Title :
Binomial linear predictive approach to texture modeling
Author :
Becchetti, Claudio ; Campisi, Patrizio
Author_Institution :
Infocom Dept., Rome Univ., Italy
Abstract :
We present a parametric texture analysis and synthesis method based on a sparse autoregressive model. The method is aimed at reducing the number of parameters necessary to define the stochastic behavior of a random field representing a given texture. For this purpose, it systematically selects the dominant contributions to the pixel cross-correlation. The method is employed in the first stage of a nonlinear texture model. Dimensional and computational advantages as well as application limits are discussed
Keywords :
autoregressive processes; correlation methods; feature extraction; image texture; linear systems; parameter estimation; prediction theory; random processes; binomial linear predictive approach; computational advantages; feature extraction; nonlinear texture model; parametric texture analysis; parametric texture synthesis method; pixel cross-correlation; random field; sparse autoregressive model; stochastic behavior; texture modeling; Autocorrelation; Bit rate; Context modeling; Electronic mail; Minimax techniques; Predictive models; Signal synthesis; Statistical analysis; Stochastic processes; Testing;
Conference_Titel :
Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on
Conference_Location :
Santorini
Print_ISBN :
0-7803-4137-6
DOI :
10.1109/ICDSP.1997.628560
