Image sequence segmentation using the gradient structure tensor method and self-organizing map

Tin Mon Mon Swe, T. Kondo, W. Kongprawechnon
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Abstract

This paper presents a technique for segmenting image sequences using the gradient structure tensor method (GSTM) and the self-organizing feature map neural network technique (SOM). GSTM accurately and robustly estimates motion vectors in an image sequence, while SOM classifies the estimated motion vectors in an unsupervised manner. Consequently, the segmentation of an image sequence is achieved. Simulation results show that the combination of the two techniques is successful for both synthetic and real image sequences.
基于梯度结构张量法和自组织映射的图像序列分割
本文提出了一种基于梯度结构张量法(GSTM)和自组织特征映射神经网络技术(SOM)的图像序列分割技术。GSTM能准确鲁棒地估计图像序列中的运动向量,而SOM对估计的运动向量进行无监督分类。因此,实现了图像序列的分割。仿真结果表明,两种技术的结合对合成图像序列和真实图像序列都是成功的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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