细胞图像轮廓检测器的非线性参数推导

C. Florea, C. Vertan, L. Florea, Alina Sultana
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引用次数: 5

摘要

本文采用参数化的方法对广泛使用的对数图像处理(LIP)模型进行了扩展。上述模型的数学结构为锥空间或向量空间。一旦数学研究确定了这些结构的边界,已知模型的参数扩展就很简单了。实验结果表明,在LIP模型下应用拉普拉斯边缘检测技术可以获得优异的性能。在本文中,我们将证明参数化不仅增加了灵活性,而且可能导致更高的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-linear parametric derivation of contour detectors for cellular images
This paper proposes an extension of the widely used Logarithmic Image Processing (LIP) models by means of parametrization. The mathematical structure of the mentioned models is that of cone or vector space. Once the mathematical investigation defined the boundaries of these structures, parametric extensions of the known models are straight-forward. It has been showed that the implementation of Laplacian edge detector techniques under the LIP model yields superior performance. In this paper we shall prove that parametrization not only adds flexibility, but may also lead to superior quality.
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