基于谱图小波变换的形状分析

J. Leandro, R. M. C. Junior, R. Feris
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引用次数: 4

摘要

本文描述了一个基于谱图小波变换的星系形态表征框架。用随机选择的若干点对星系图像进行采样,其Delaunay三角剖分结果为任意图。将与图顶点相关的像素的5 × 5附近的平均强度值分配给相应的图顶点。将与每对顶点之间的光度距离成反比的权值分配给各自的图边。该谱图小波变换由实值顶点加权图计算得到高维特征向量,通过主成分分析将其降阶为二维特征向量。通过两个案例研究,即分析(i)来自形状的二维二值图像的案例研究和(ii)来自星系的二维灰度图像的初步结果,对拟议的框架进行了评估。所获得的结果表明,该框架适用于星系图像的表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shape Analysis Using the Spectral Graph Wavelet Transform
The present work describes a framework for morphological characterization of galaxies based on the Spectral Graph Wavelet Transform. A galaxy image is sampled with a number of points randomly chosen, whose Delaunay triangulation results in an arbitrary graph. The average intensity value in a 5 × 5 vicinity of a pixel related to a graph vertex is assigned to the corresponding graph vertex. A weight inversely proportional to the photometric distance between each pair of vertices is assigned to the respective graph edge. The Spectral Graph Wavelet Transform is computed from this weighted graph with real-valued vertices yielding a high-dimensional feature vector, which is reduced to a two dimensional vector through Principal Component Analysis. The proposed framework has been assessed through two case studies, namely, the case study of analyzing (i) 2D binary images from shapes and preliminary results of (ii) 2D gray tone images from galaxies. The obtained results imply the suitability of this framework for the characterization of galaxies images.
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