| 1. |
- Arcelli, Carlo, et al.
(författare)
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Computing and analysing convex deficiencies to characterise 3D complex objects
- 2005
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Ingår i: Image and Vision Computing: Discrete Geometry for Computer Imagery. ; 23:2, s. 203-211
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Tidskriftsartikel (refereegranskat)abstract
- Entities such as object components, cavities, tunnels and concavities in 3D digital images can be useful in the framework of object analysis. For each object component, we first identify its convex deficiencies, by subtracting the object component from a covering polyhedron approximating the convex hull. Watershed segmentation is then used to decompose complex convex deficiencies into simpler parts, corresponding to individual cavities, concavities and tunnels of the object component. These entities are finally described by means of a representation system accounting for the shape features characterising them.
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| 2. |
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| 3. |
- Borgefors, Gunilla, et al.
(författare)
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Discrete Skeletons from Distance Transforms in 2D and 3D
- 2008
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Ingår i: Medial Representations. - Netherlands : Springer Verlag. ; , s. 155-190
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- We present discrete methods to compute the digital skeleton of shapes in 2D and 3D images. In 2D, the skeleton is a set of curves, while in 3D it will be a set of surfaces and curves, the surface skeleton, or a set of curves, the curve skeleton. A general scheme could, in principle, be followed for both 2D and 3D discrete skeletonization. However, we will describe one approach for 2D skeletonization, mainly based on marking in the distance transform the shape elements that should be assigned to the skeleton, and another approach for 3D skeletonization, mainly based on iterated element removal. In both cases, the distance transform of the image will play a key role to obtain skeletons reflecting important shape features such as symmetry, elongation, and width.
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| 4. |
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| 5. |
- Nyström, Ingela, et al.
(författare)
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Skeletonization in 3D Discrete Binary Images
- 2005
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Ingår i: Handbook of Pattern Recognition and Computer Vision, 3rd edition. - : World Scientific, Singapore. - 9812561056 ; , s. 137-156
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Bokkapitel (populärvet., debatt m.m.)abstract
- Skeletonization is a way to reduce dimensionality of digital objects. Here, we present in detail an algorithm that computes the curve skeleton of a solid object, i.e., an object without cavities, in a 3D binary image. The algorithm consists of three main steps. During the first step, the surface skeleton is detected, by directly marking in the distance transform of the object the voxels that should be assigned to the surface skeleton. The curve skeleton is then computed by iteratively thinning the surface skeleton, during the second step. Finally, the third step is performed to reduce the curve skeleton to unit width and to prune, in a controlled manner, some of its peripheral branches.
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| 6. |
- Sanniti di Baja, Gabriella, et al.
(författare)
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Discrete 3D tools applied to 2D grey-level images
- 2005
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Ingår i: Image Analysis and Processing - ICIAP 2005. - 3540288694 ; , s. 229-236
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Konferensbidrag (refereegranskat)abstract
- 2D grey-level images are interpreted as 3D binary images, where the grey-level plays the role of the third coordinate. In this way, algorithms devised for 3D binary images can be used to analyse 2D grey-level images. Here, we present three such algorithms. The first algorithm smoothes a 2D grey-level image by flattening its geometrical and grey-level peaks while simultaneously filling in geometrical and grey-level valleys, regarded as non significant in the problem domain. The second algorithm computes an approximation of the convex hull of a 2D grey-level object, by building a covering polyhedron closely fitting the corresponding object in a 3D binary image. The result obtained is convex both from the geometrical point of view and as concerns grey-levels. The third algorithm skeletonizes a 2D grey-level object by skeletonizing the top surface of the object in the corresponding 3D binary image.
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