1 and the maximum region homogeneity conditions

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: te segmentation (equation 5.1): And the maximum region homogeneity conditions (equations 5.31, 5.32): Three basic approaches to region growing exist: region merging, region splitting and split-and-merge region growing. Faculty of Engineering Robotics Technology MECH 4041 22 B.Eng (Hons.) Mechatronics S. Venkannah Mechanical and Production Engineering Department Region merging starts with an over-segmented image in which regions satisfy equation 5.31. Regions are merged to satisfy condition (5.32) as long as equation (5.31) remains satisfied. Region splitting is the opposite of region merging. Region splitting begins with an under-segmented image which does not satisfy condition (5.31). Therefore the existing image regions are sequentially split to satisfy conditions (5.1), (5.31), and (5.32) A combination of splitting and merging may result in a method with the advantages of both other approaches. Split and merge approaches typically use pyramid image representations. Because both split-and-merge processing options are available, the starting segmentation does not have to satisfy either condition (5.31) or (5.32). In watershed segmentation, catchment basins represent the regions of the segmented image. The first, watershed segmentation approach starts with finding a downstream path from each pixel of the image to the local minima of image surface altitude. A catchment basin is then defined as the set of pixels for which their respective downstream paths all end up in the same altitude minimum. In the second approach, each gray level minimum represents one catchment basin and the strategy is to start filling the catchment basins from the bottom. Images segmented by region growing methods often contain either too many regions (under growing) or too few regions (over-growing) as a result of non-optimal parameter setting. To improve classification results, a variety of post-processors has been developed. Simpler post-processors decrease the number of small regions in the segmented image. More complex post-processing may combine segmentation information obtained from region growing and edge...
View Full Document

Ask a homework question - tutors are online