Range segmentation is the task of
segmenting (dividing) a ''
range image
Range imaging is the name for a collection of techniques that are used to produce a 2D image showing the distance to points in a scene from a specific point, normally associated with some type of sensor device.
The resulting range image has pix ...
'', an image containing
depth information for each pixel, into
segments (regions), so that all the points of the same
surface
A surface, as the term is most generally used, is the outermost or uppermost layer of a physical object or space. It is the portion or region of the object that can first be perceived by an observer using the senses of sight and touch, and is ...
belong to the same region, there is no overlap between different regions and the union of these regions generates the entire
image.
Algorithmic approaches
There have been two main approaches to the range segmentation problem: ''region-based range segmentation'' and ''edge-based range segmentation''.
Region-based range segmentation
Region-based range segmentation algorithms can be further categorized into two major groups: ''parametric model-based'' range segmentation algorithms and ''region-growing'' algorithms.
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).
Region-growing algorithms start by segmenting an image into initial regions. These regions are then merged or extended by employing a region growing strategy. 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.
Edge-based range segmentation
Edge-based range segmentation algorithms are based on
edge detection
Edge detection includes a variety of mathematical methods that aim at identifying edges, curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuitie ...
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. 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
Least squares 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.
[Powell, M. W., Bower, K., Jiang, X., and Bunke, H., ''"Comparing Curved-Surface Range Image Segmenters"'' Proceedings of 6th International Conference on Computer Vision (ICCV), Bombay, India, pp. 286–291, 1998.]
See also
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Image segmentation
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects ( sets of pixels). The goal of segmentation is to simpli ...
*
Image-based meshing Image-based meshing is the automated process of creating computer models for computational fluid dynamics (CFD) and finite element analysis (FEA) from 3D image data (such as magnetic resonance imaging (MRI), computed tomography (CT) or microtomogra ...
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Quantization (image processing)
Quantization, involved in image processing, is a lossy compression technique achieved by compressing a range of values to a single quantum (discrete) value. When the number of discrete symbols in a given stream is reduced, the stream becomes more ...
References
External links
IEEE International Conference on Computer Vision and Pattern Recognition''(CVPR)''
6th International Conference on Computer Vision, Bombay, 1998''(ICCV)''
Image segmentation