Fine feature sensitive marching squares. Issue 9 (18th August 2017)

• Record Type: Journal Article
• Authors: Gong, Sui; Newman, Timothy S.
• Is Part Of: IET image processing. Volume 11:Issue 9(2017)
• Page Start: 796
• Page End: 802
• Publication Date: 2017-08-18
• Subjects: image processing; fine feature sensitive marching square -- contouring method -- two‐dimensional scalar‐value image dataset -- grey‐scale intensity imaging -- digital camera -- X‐ray device -- FFS‐MS -- volume preserving marching cubes algorithm -- 2D scalar‐value image dataset; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.1124
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : A new contouring method for producing region boundaries in two‐dimensional (2D) scalar‐value image datasets (such as grey‐scale intensity images from a digital camera or X‐ray device) with sub‐pixel precision is introduced here. The method, fine feature sensitive marching squares (FFS‐MS), extends MS isocontouring to produce an isocontour that preserves fine‐scale features (which are often incorrectly recovered by standard MS). This extension is the 2D analogue of Kaneko and Yamamoto's volume preserving marching cubes algorithm. It has several phases. First, it recovers an isocontour using standard MS. Then, it produces a new dataset with data values estimated by treating the recovered contour as the actual boundary. Using this new dataset, it next compares that dataset's estimated data values with the data values at corresponding locations in the original dataset. Finally, the method adjusts the original dataset's pixel values at every pixel location, where there is a high discrepancy between the original data value and the estimated data value. It iteratively repeats its phases until an optimality criterion is satisfied. Experimental analyses of FFS‐MS are also presented. The analyses focus on FF recovery in comparison with the standard MS.