Edge detection is the process of segmenting an image by detecting discontinuities in brightness. Several standard segmentation methods have been widely used for edge detection. However, due to inherent quality of images, these methods prove ineffective if they are applied without any preprocessing. In this paper, an image pre-processing approach has been adopted in order to get certain parameters that are useful to perform better edge detection with the standard edge detection methods. The proposed preprocessing approach involves median filtering to reduce the noise in image and then edge detection technique is carried out. Finally, Standard edge detection methods can be applied to the resultant pre-processing image and its Simulation results are show that our pre-processed approach when used with a standard edge detection method enhances its performance.
A critical and important stage in microstructure image analysis is segmentation, because the segmentation method has direct impact on the end results of analysis. The main aim of this paper is to determine appropriate segmentation method for microstructure image analysis and quantification. In this work, some popular segmentation methods, namely, Otsu's automatic threshold, watershed, uni-grid active contour method and multi-grid active contour methods have been investigated. The reliability of the segmentation methods is tested by determining the volume fraction of phases present in microstructure images of materials of known chemical composition. The experimentation is done using microstructure images of cast iron of various compositions. The experimental results are compared with expected values of volume fraction. The active contour multi-grid segmentation model is found to yield better results within the practical limits as compared to manual and other automated methods.
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