Marker-Free Registration of Forest Terrestrial Laser Scanner Data Pairs With Embedded Confidence Metrics

Kelbe, David; Aardt, Jan van; Romanczyk, Paul; Leeuwen, Martin van; Cawse‐Nicholson, Kerry

doi:10.1109/tgrs.2016.2539219

Cited by 68 publications

(39 citation statements)

References 31 publications

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“…Another problematic challenge is that with an increasing number of scans, noise can be more easily produced because of the more challenging co-registration of multiple scans. This phenomenon was likewise confirmed by other existing studies (e.g., [5,6,42,79]).…”

Section: Comparison Of Scan Variants Tree Detection and Dbh Measuresupporting

confidence: 91%

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

et al. 2019

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This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate.

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Section: Comparison Of Scan Variants Tree Detection and Dbh Measuresupporting

confidence: 91%

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

et al. 2019

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“…Figure 10 and Figure 11 show similar tendency with Figure 8 and Figure 9 ; thus, similar conclusions can be drawn. MAE is below 2 m when t G is in the range of [ 3 , 40 ], which is much wider than the that of d G . We also note that t G has more influence on the efficiency than d G , by comparing Figure 8 d and Figure 10 d. The best results are achieved when t G is approximately 1% to 10% of the shortest edge of the point cloud’s bounding box.…”

Section: Experiments and Discussionmentioning

confidence: 93%

“…The semantic information was also used in [ 39 ], in which Yang et al detected features points based on semantic feature and the matching was processed by searching for corresponding triangles constructed and eliminate from the feature points. Kelbe et al [ 40 ] calculated the transformation parameters for the forest TLS data based on the results of tree detection, which can be obtained from some previous work [ 41 , 42 ]. Some other geometric elements are also used in the registration, such as salient directions [ 43 ], cylindrical and polygonal objects [ 44 ], fitted objects in industrial environments [ 7 ] and other fitted geometric primitives [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS

Chen

Wang²,

Wang

et al. 2017

Sensors

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Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency.

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“…However, corner points in medical images are not necessarily the best points for registration, and the images of different patients sometimes vary greatly, which may cause the problem of mismatching keypoints. Therefore, many registration methods rely on placing human contact points in the image to help coordinate transformation [51]. In the registration of medical images, specific anatomical points are usually selected as keypoints, and these specific points need to be manually marked by doctors to be obtained.…”

Section: A Image Registration Based On Keypointsmentioning

confidence: 99%

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

Chen

et al. 2020

IEEE Access

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Automatic detection of specific anatomical points of interest (POIs) in head magnetic resonance imaging (MRI) is a technical bottleneck in medical image registration and big data analysis for head diseases. A technique for automatically retrieving POIs in head MRI scans is explored in this study. A Haar-like feature of the image is introduced to generate the feature description vector of the POI, and the most appropriate standard vector is selected from multiple marked images. A fixed five-point joint feature description is proposed for solid POI detection, and an adaptive three-point joint feature description is proposed for cavity POI detection. A total of 516 head MRI volumes were used for anatomical point detection. A solid point detection experiment was conducted using the POIs of the right/left internal acoustic pore (RIA/LIA). The POIs of the right/left ascending segment of the internal carotid artery in the posterior cavernous sinus (RAS/LAS) were used in a cavity point detection experiment. The experimental results show that the accuracies of the solid point detection for LIA and RIA are 81.8% and 84.7%, respectively. Those of cavity point detection for LAS and RAS are 66.7% and 76.2%. The performance of the proposed method is better than those of BRIEF and SIFT algorithm. The proposed method can facilitate the marking of anatomical points for doctors, thus providing technical support for head image automatic registration and big data analysis for head diseases.

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Marker-Free Registration of Forest Terrestrial Laser Scanner Data Pairs With Embedded Confidence Metrics

Cited by 68 publications

References 31 publications

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

Entropy-Based Registration of Point Clouds Using Terrestrial Laser Scanning and Smartphone GPS

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

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