PARAMETER ESTIMATION IN A GIBBS-MARKOV FIELD TEXTURE MODEL BASED ON A CODING APPROACH

被引：0

作者：

Martinez, Jorge ^{[1
]}

Pistonesi, Silvina ^{[1
]}

Cristina Maciel, Maria ^{[1
]}

Georgina Flesia, Ana ^{[2
]}

机构：

[1] Univ Nacl Sur, Dept Matemat, Bahia Blanca, Buenos Aires, Argentina

[2] Univ Nacl Cordoba, Fac Matemat Astron & Fis, Cordoba, Argentina

来源：

2018 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP) | 2018年

关键词：

Parameter estimation; gibbs-markov model; conditional least square; coding; parallel computing; STATISTICAL-ANALYSIS;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we present a novel approach of the Conditional Least Square (CLS) estimator based on a coding scheme, for estimating the parameter vector associated with an Auto-Binomial model. This method provides a parallel solver for the estimation process. In order to illustrate the performance of the proposed approach, we carried out a Monte Carlo study and a real application for landscape classification using a high-resolution Pleiades-1A satellite image. Experimental results demonstrated the effectiveness of our estimation approach as well as CLS method, but in a lower runtime.

引用

页码：105 / 109

页数：5

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