Omnidirectional Edge Detection Based on Two-dimensional Log Butterworth Filters in Frequency Domain

Abstract

Abstract: In this paper, an improved algorithm of omnidirectional edge detection based on two-dimension Log Butterworth filter is proposed to satisfy the need of the nonlinear recognition mechanism in the image processing. The edge detection using the proposed algorithm involves Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) in frequency domain. The two-dimension Log Butterworth filter is proposed by introducing the Log function into the Butterworth filter. When the length and width of image is different, the centre frequency is located at an ellipse with the ratio of long axis to minor axis being equal to the length-width ratio of the original image. Thus, this filter can be expressed with a variable of angle. In order to obtain the optimal parameters range, the parameters of the two-dimension Log Butterworth filter are normalized. Then F-measure and PSNR (Peak Signal to Noise Ratio) are introduced into the determination of the range of optimal parameters. Meanwhile, the numbers of multiplication and addition, and computation time for edge detection with different size of image are used to compare the efficiency of the proposed algorithm and a traditional edge detector (Canny detector). Finally, edge detections of BSDS (The Berkeley Segmentation Dataset and Benchmark) images and high spatial resolution remotely sensed imageries using the proposed algorithm are evaluated and analyzed. The results of evaluation and analysis show that the proposed algorithm can be used to detect edges from images efficiently.

Key words: Log Butterworth filter, omnidirectional edge detection, frequency domain, the number of multiplications and additions

CLC Number:

TP751.1

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