Decision Trees for Fast Thinning Algorithms

Abstract

We propose a new efficient approach for neighborhood exploration, optimized with decision tables and decision trees, suitable for local algorithms in image processing. In this work, it is employed to speed up two widely used thinning techniques. The performance gain is shown over a large freely available dataset of scanned document images.

Connected Components Labeling on DRAGs

“…The missing step for this work is the ability of including the prediction stage introduced in [17] within a unique framework, and being able of solving the complete problem in feasible time. Another extension is the application of DRAGs to other binary image problems such as thinning or mathematical morphology [23].…”

One DAG to Rule Them All

“…Furthermore, Guo and Hall [62] proposed a solution to better cope with 2×2 squares and diagonal lines, obtaining skeletons with less stair case artifacts. Besides the already mentioned LUT technique, iterations based on decision trees have been proposed in [63], to further speedup the ZS algorithm. These solutions have been proposed some decades ago, but are still commonly used [25], [26], [27] and included in many image processing libraries, such as OpenCV.…”

Development of a Smart Hallway for Marker-Less Human Foot Tracking and Stride Analysis

“…(7). A connected component filter [23] with 1000 pixels connected area cut-off was applied to the (-) image and output was a Binary Filtered Background Subtracted image ( -) ). The -) image was modified based on the (-) , pixel locations with zero value in (-) were assigned to zero in (-) frame (8).…”

“…A connected component filter [23] with 1000 pixels connected area cut-off was applied to the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${BBS}^{\left ({j }\right)}$ \end{document} image and output was a Binary Filtered Background Subtracted image \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$({BFBS}^{\left ({j }\right)})$ \end{document} . The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${BS}^{(j)}$ \end{document} image was modified based on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${BFBS}^{\left ({j }\right)}$ \end{document} , pixel locations with zero value in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${BFBS}^{\left ({j }\right)}$ \end{document} were assigned to zero in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${BS}^{(j)}$ \end{document} frame (8) .…”