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<title>Range segmentation</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Range segmentation</span></span>

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<div id="mw-content-text" class="mw-body-content mw-content-ltr" lang="en" dir="ltr"><div class="mw-content-ltr mw-parser-output" lang="en" dir="ltr"><p><b>Range segmentation</b> is the task of <a href="Segmentation_(image_processing)" class="mw-redirect" title="Segmentation (image processing)">segmenting</a> (dividing) a <i><a href="Range_image" class="mw-redirect" title="Range image">range image</a></i>, an image containing <a href="Depth_map" title="Depth map">depth information</a> for each pixel, into <a href="Image_segment" class="mw-redirect" title="Image segment">segments</a> (regions), so that all the points of the same <a href="Computer_representation_of_surfaces" title="Computer representation of surfaces">surface</a> belong to the same region, there is no overlap between different regions and the union of these regions generates the entire <a href="Image" title="Image">image</a>.

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<div class="mw-heading mw-heading2"><h2 id="Algorithmic_approaches">Algorithmic approaches</h2></div>

<p>There have been two main approaches to the range segmentation problem: <i>region-based range segmentation</i> and <i>edge-based range segmentation</i>.

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<div class="mw-heading mw-heading3"><h3 id="Region-based_range_segmentation">Region-based range segmentation</h3></div>

<p>Region-based range segmentation algorithms can be further categorized into two major groups: <i>parametric model-based</i> range segmentation algorithms and <i>region-growing</i> algorithms.

</p><p>Algorithms of the first group are based on assuming a parametric surface model and grouping data points so that all of them can be considered as points of a surface from the assumed parametric model (an instance of that model).<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>

</p><p>Region-growing algorithms start by segmenting an image into initial regions. These regions are then merged or extended by employing a region growing strategy.<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> The initial regions can be obtained using different methods, including iterative or random methods. A drawback of algorithms of this group is that in general they produce distorted boundaries because the segmentation usually is carried out at region level instead of pixel level.

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<div class="mw-heading mw-heading3"><h3 id="Edge-based_range_segmentation">Edge-based range segmentation</h3></div>

<p>Edge-based range segmentation algorithms are based on <a href="Edge_detection" title="Edge detection">edge detection</a> and labeling edges using the jump boundaries (discontinuities). They apply an edge detector to extract edges from a range image. Once boundaries are extracted, edges with common properties are clustered together. A typical example of edge-based range segmentation algorithms is presented by Fan et al.<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> The segmentation procedure starts by detecting discontinuities using zero-crossing and curvature values. The image is segmented at discontinuities to obtain an initial segmentation. At the next step, the initial segmentation is refined by fitting quadratics whose coefficients are calculated based on the <a href="Least_squares" title="Least squares">Least squares</a> method. In general, a drawback of edge-based range segmentation algorithms is that although they produce clean and well defined boundaries between different regions, they tend to produce gaps between boundaries. In addition, for curved surfaces, discontinuities are smooth and hard to locate and therefore these algorithms tend to under-segment the range image. Although the range image segmentation problem has been studied for a number of years, the task of segmenting range images of curved surfaces is yet to be satisfactorily resolved.<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup>

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<ul><li><a href="Image_segmentation" title="Image segmentation">Image segmentation</a></li>

<li><a href="Image-based_meshing" title="Image-based meshing">Image-based meshing</a></li>

<li><a href="Quantization_(image_processing)" title="Quantization (image processing)">Quantization (image processing)</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

<li id="cite_note-1"><span class="mw-cite-backlink"><b><a href="#cite_ref-1">^</a></b></span> <span class="reference-text">Bab-Hadiashar, A. and Gheissari, N., <i>"Range Image Segmentation Using Surface Selection Criterion"</i>, IEEE Transactions on Image Processing, 15(7), pp. 2006–2018, 2006.</span>

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<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text">Jaklič, A. and Leonardis, A. and Solina, F. <a rel="nofollow" class="external text" href="https://www.springer.com/gp/book/9780792366010">"Segmentation and Recovery of Superquadrics"</a>. <i>Computational imaging and vision</i> 20, Kluwer/Springer, 2000.</span>

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<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text">Faugeras, O. D. and Hebert, M., <i>"Segmentation of Range Data into Planar and Quadratic Patches,"</i> Proceedings of IEEE conference on Computer Vision and Pattern Recognition , Arlington,VA, pp. 8–13, June 1983.</span>

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<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text">Medioni, G. and Parvin, B., <i>"Segmentation of Range Images into planar Surfaces by Split and Merg"</i>, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 415–417, 1986.</span>

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<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text">Fan, T. U., Medioni, G., and Nevatia, R., <i>"Recognising 3-D Objects Using Surface Descriptions"</i>, IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 11, pp. 1140–1157, Nov, 1989.</span>

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<li id="cite_note-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-6">^</a></b></span> <span class="reference-text">Powell, M. W., Bower, K., Jiang, X., and Bunke, H., <i>"Comparing Curved-Surface Range Image Segmenters"</i> Proceedings of 6th International Conference on Computer Vision (ICCV), Bombay, India, pp. 286–291, 1998.</span>

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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external text" href="https://web.archive.org/web/20011004105649/http://www.cvpr.org/">IEEE International Conference on Computer Vision and Pattern Recognition</a> <i>(CVPR)</i></li>

<li><a rel="nofollow" class="external text" href="http://www.umiacs.umd.edu/users/lsd/iccv/">6th International Conference on Computer Vision, Bombay, 1998</a> <i>(ICCV)</i></li></ul></div><div><div class="zim-footer">

This article is issued from <a class="external text" title="Last edited on 2020-05-18" href="https://en.wikipedia.org/wiki/?title=Range_segmentation&oldid=957452811">Wikipedia</a>. The text is available under <a class="external text" href="https://creativecommons.org/licenses/by-sa/4.0/deed.en">Creative Commons Attribution-Share Alike 4.0</a> unless otherwise noted. Additional terms may apply for the media files.

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