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首页> 外文会议>Biennial Australian Pattern Recognition Society Conference(DICTA2003) v.2; 2003; Sydney; AU >Multiresolution Image Segmentation with Border Smoothness for Scalable Object-Based Wavelet Coding
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Multiresolution Image Segmentation with Border Smoothness for Scalable Object-Based Wavelet Coding

机译：具有边界平滑度的多分辨率图像分割用于可扩展的基于对象的小波编码

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摘要

This paper introduces a multiresolution image segmentation algorithm for scalable object-based wavelet coding applications. This algorithm is based on discrete wavelet transform and multiresolution Markov random field (MMRF) modelling. The major contribution of this work is to add spatial scalability and border smoothness in the segmentation algorithm usable for object-based wavelet coding algorithm. To optimize the segmentation/extraction of objects/regions of interest in all scales of the wavelet pyramid, with scalability constraint, a multiresolution analysis is incorporated into the objective function of MMRF segmentation algorithm. The proposed algorithm improves border smoothness in all regions, particularly in lower resolutions. In addition to scalability between objects/regions in different levels, the proposed algorithm outperforms the standard multiresolution segmentation algorithms, in both objective and subjective tests, in yielding an effective segmentation that supports scalable object-based wavelet coding.
机译：本文针对可扩展的基于对象的小波编码应用，介绍了一种多分辨率图像分割算法。该算法基于离散小波变换和多分辨率马尔可夫随机场（MMRF）建模。这项工作的主要贡献是在可用于基于对象的小波编码算法的分割算法中增加了空间可伸缩性和边界平滑度。为了在小波金字塔的所有尺度上优化目标对象/区域的分割/提取，并具有可伸缩性约束，将多分辨率分析合并到MMRF分割算法的目标函数中。该算法提高了所有区域的边界平滑度，尤其是在较低分辨率下。除了在不同级别的对象/区域之间的可伸缩性之外，在产生可支持可伸缩的基于对象的小波编码的有效分割方面，所提出的算法在客观和主观测试方面均优于标准的多分辨率分割算法。

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