Image Segmentation Segmentation subdivides an image into its constituent regions or groups. The level to which the subdivision is carried depends on the problem being solved. That is, segmentation should stop when the objects of interest in an application have been isolated. e.g. automated inspection of electronic assemblies; specific anomalies; missing components or broken connection paths.
Image Segmentation Segmentation algorithms ; Two categories based on two basic properties of intensity values : discontinuity and similarity First Category : Abrupt changes in intensity ; edges Second Category : partitionning of regions which are similar according to a set of predefined criteria. e.g. Thresholding, region growing, region splitting and merging.
Image Segmentation First Category : Points, Lines, Edges
Detection of discontinuities Points, lines, edges The most common way R = w1*z1 + w2*z2 + ……+ w9*z9
Point detection R T T = Threshold Figure 10.2 (a) point detection mask
Point detection (b) X-ray image of a turbine blade with porosity (c) Result of point detection mask (d) Result of point detection mask with threshold Figure 10.2
Line detection – A Suitable Mask in desired direction – Thresholding Figure 10.3 Line masks
• Example: Line detection -45º Mask Thresholding Figure 10.4 Illustration of line detection (a) ,(b),(c)