A Novel Edge Detection Technique using Gray-Level Spatial Correlation based on Statistical Parameters
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Abstract
In this paper we propose a novel edge detection technique. Edges and noise pixels exhibits similar characteristics, unfortunately most of the existing techniques fails in generating the satisfactory results because they are not satisfying the criterion parameters of qualitative evaluation. There by, we introduce Gray Level Spatial Correlation technique based on a statistical parameter computed as the absolute difference between Global mean of entire image and local mean of a 3X3 map. The resultant similarity values other than 9 evident object edges; hence this technique eliminates most of the noise pixels and highlights the object edges. This approach can be further extended to describe the misclassification region or Region of Interest (ROI) for many supervised segmentation and threshold estimation methods. The results obtained are very promising in comparison with standard existing techniques. This method converges at low time complexities.
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Keywords: Edge detection, GLSC, ROI, misclassification.
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