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Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it...
Semi-automatic methods are regarded as more practical than automatic methods to obtain and/or update the vector road data. But road tracking methods lose their efficiencies when they are applied to tr...
Multi-scale segmentation is an essential step toward higher level image processing in remote sensing. This paper presents a new multi-scale segmentation method based on Statistical Region Merging (SRM...
Segmentation, i.e. the labelling of objects in image data, is a crucial step in many medical imaging processing tasks, e.g. operation planning, radio therapy or diagnostics. Seeded region growing is ...
Stereo vision and LIDAR continue to dominate standoff 3D measurement techniques in photogrammetry although the two techniques are normally used in competition. Stereo matching algorithms generate dens...
The paper considers the problem of object segmentation and shape recognition in discrete noisy data. Two different algorithms combine region growing techniques with principal component analysis. The...
Applying the region growing to the Kohonen’s self-organizing feature map, a non-supervised classifier for remotely sensed imagery data is proposed. If the self-organizing feature map is made large e...
At present, airborne laser scanner systems are one of the most frequent methods used to obtain digital terrain elevation models. While having the advantage of direct measurement on the object, the poi...
As three-dimensional range scanners make large point clouds a more common initial representa-tion of real world objects, a need arises for algorithms that can efficiently process point sets. In this p...

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