小波变换边缘检测特性分析
THE FEATURE ANALYSIS OF IMAGE EDGE DETECTION WITH WAVELETS
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摘要: 本文一改以往的以仿真的感性效果作为信号边缘检测质量的效果评价方法,提出小波变换边缘检测定位精度和抗噪声能力量化分析方法。基于小波变换的边缘检测算法,物理意义上是一个先平滑,再进行边缘检测的过程,其边缘检测特性与边缘类型和尺度大小有关。随尺度增大,定位偏差增大,反映了小波变换局部化特征强弱对边缘检测特性的影响。本文给出了不含噪声和含有噪声情况下,典型边缘定位精度的量化表述。Abstract: A quantitative analysis on the local precision and the ability against noise for image edge detection with wavelets (IEDW) has been done in this paper. With an appropriate wavelet function, IEDW algorithm is equal with a process that consists of two child processes: denoising and edge detecting. The feature is relative to the type of edges and the scales. The influence of the scale is a morror of the local feature of wavelet transform.
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秦前清,扬中凯.实用小波分析.西安:西安电子科技大学出版社,1994, 80-92.[2]吴立德.计算机视觉.上海:上海复旦大学出版社,1992, 20-33.[3]Mallat S. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. on Pattern and Machine Intelligence, 1989, PAMI-11(7): 674-692.[4]Mallat S, Zhong S. Characterizaton of signals from multiscale edges. IEEE Trans. on Pattern and Machine Intelligence, 1992, PAMI-14(7): 710-731. -
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