Non Linear Robust Edge Detector for Noisy Images

Atluri Srikrishna*, B. Eswara Reddy**, M. Pompapathi***
* Professor and Head, Department of Information Technology at RVR & JC College of Engineering, Guntur, India.
** Professor, Department of Computer Science, JNTUA College of Engineering, Anantapur, India.
*** Assistant Professor, Department of Information Technology, RVR & JC College of Engineering, Guntur, India.
Periodicity:October - December'2015
DOI : https://doi.org/10.26634/jip.2.4.3688

Abstract

Identification of edge pixels of the noisy signal without doing regularization is still a challenging problem for researchers. There exists different methods and each method has its own assumptions, advantages, and limitations. Author propose a Non Linear Robust Edge Detector (NLRED), which uses n x n window in order to detect the edges of all possible orientations at noisy images. The proposed method partitions the neighbors of the pixel which is under observation for edge candidature into two sub regions based on differences in the local gray level value. The proposed method calculates test statistic for pixel of each sub region by calculating mean, the placement of each member, index of variability and test statistics. The test statistics with maximum value is considered based on two sub regions. These statistics are calculated for eight different orientations. Among these, the statistic with minimum value is considered for edge candidature. The performance is measured in terms of Figure of Merit (FOM) to show the efficiency of the proposed method by comparing with statistical edge detector CANNY approach, in order to detect all possible edges.

Keywords

Regularization, Canny Edge Image, Test Statistic, Figure of Merit, Kernel

How to Cite this Article?

Srikrishna, A., Reddy, B.E., and Pompapathi, M. (2015). Non Linear Robust Edge Detector for Noisy Images. i-manager’s Journal on Image Processing, 2(4), 30-38. https://doi.org/10.26634/jip.2.4.3688

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