Logarithmic Edge Detection with Applications

Abstract

Abstract—In real world machine vision problems, numerous issues such as variable scene illumination make edge and object detection difficult. There exists no universal edge detection method which works well under all conditions. In this paper, we propose a logarithmic edge detection method based on Parameterized Logarithmic Image Processing (PLIP) and a four-directional Sobel method, achieving a higher level of independence from scene illumination. We present experimental results for this method, and compare results of the algorithms against several leading edge detection methods, such as Sobel and Canny. To compare results objectively, we use Pratt’s Figure of Merit. We demonstrate the application of the algorithm in conjunction with Edge Preserving Contrast Enhancement (EPCE), which is an image enhancement method dependent on the raw output of an edge detection kernel. This shows that the use of this edge detection algorithm results in better image enhancement, as quantified by the Logarithmic AME.

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