Automatic tree stem detection – a geometric feature based approach for MLS point clouds
TL;DR: The results show that principal direction and shape analysis are sufficient for the tree stem recognition from MLS point cloud in a relatively complex urban area.
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Abstract: Recognition of tree stem is a fundamental task for obtaining various geometric attributes of trees such as diameter, height, stem position and so on for diverse of urban application We propose a novel tree stem segmentation approach using geometric features corresponding to trees for high density MLS point data covering in urban environments The principal direction and shape of point subsets are used as geometric features Point orientation exhibits the most variance (shape of point set) of a point neighbourhood, assists to measure similarity, while shape provides the dimensional information of a group of points Points residing on a stem can be isolated by defining various rules based on these geometric features The shape characterization step is accomplished by estimating the structure tensor with principal component analysis These features are assigned to different steps of our segmentation algorithm Wrong segmentations mainly occur in the area where our rules have failed, such as vertical type objects, road poles and light post To overcome these problems, global shape is further checked The experiment is performed to evaluate the method; it shows that more than 90% of tree stems are detected The overall accuracy of the proposed method is 906% The results show that principal direction and shape analysis are sufficient for the tree stem recognition from MLS point cloud in a relatively complex urban area
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Citations
Automatic Forest Mapping at Individual Tree Levels from Terrestrial Laser Scanning Point Clouds with a Hierarchical Minimum Cut Method
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TL;DR: The main contribution of this paper is to solve the optimization of cluster combination by minimizing the proposed energy function and to extract nonphotosynthetic components through a hierarchical clustering process automatically.
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Automatic Stem Detection in Terrestrial Laser Scanning Data With Distance-Adaptive Search Radius
TL;DR: A point-based method for stem detection is proposed using single-scan TLS data, where the search radius is generated adaptively, based on the relationship between the distance and point density, to make sure that the neighborhood maintains a similar scale to the corresponding point density.
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Navigation and Mapping in Forest Environment Using Sparse Point Clouds
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References
Particle shape: a review and new methods of characterization and classification
Simon J. Blott,Kenneth Pye +1 more
TL;DR: In this article, it is shown that the most important aspects of particle form are represented by the I/L ratio (elongation ratio) and S/I ratio (flatness ratio), which can be used to classify particles in terms of 25 form classes.
605
Natural terrain classification using three-dimensional ladar data for ground robot mobility
TL;DR: This paper focuses on the segmentation of ladar data into three classes using local threedimensional point cloud statistics to represent porous volumes such as grass and tree canopy, and finally “surface” to capture solid objects like ground surface, rocks, or large trunks.
Automatic forest inventory parameter determination from terrestrial laser scanner data
TL;DR: Methods for the automatic detection of trees in terrestrial laser scanner data as well as the automatic determination of diameter at breast height (DBH), tree height and 3D stem profiles are outlined.
446
Representing local structure using tensors II
Hans Knutsson,Carl-Fredrik Westin,Mats Andersson +2 more
- 01 May 2011
TL;DR: It is shown how higher order tensors can be estimated using a generalization of the same simple formulation as a number of known structure tensor algorithms by formulating them in monomial filter set terms.