Automatic segmentation and model identification in unordered 3D-point cloud

Ahn, Sung Joon; Effenberger, Ira; Rauh, Wolfgang; Cho, Hyungsuck; Westkmper, E.

doi:10.1117/12.467726

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…5.2 [11]). Note that the determination of the minimum distance points is the time-consuming element in the overall procedure.…”

Section: Domain Volume For Measurement Pointsmentioning

confidence: 96%

“…Thus, object recognition -including object detection and identification -is an essential element of object reconstruction. Whilst fully automatic object recognition in a point cloud may only be possible by analyzing all the available information mentioned above, a semi-automatic procedure is presented in this chapter for object recognition in a point cloud, which requires minimal human operator assistance [11].…”

Section: Object Reconstruction From Unordered Point Cloudmentioning

confidence: 99%

“…Also, to achieve a high automation degree of the overall procedures of the segmentation and outlier elimination in point cloud, the properties of implicit features are utilized. The properties of the applied fitting algorithms and of implicit features [11] …”

Section: Semi-automatic Object Recognitionmentioning

confidence: 99%

“…5.1) was used. The procedure starts by picking one or two seed points in a point cloud and choosing a low-level model type [9], [11]. If necessary, the model type can be updated, e.g.…”

Section: Segmentation Outlier Elimination and Model Fittingmentioning

confidence: 99%

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Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space

Ahn¹

2004

Lecture Notes in Computer Science

102

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PrefaceLeast-squares is a term which implies that the model parameters are determined by minimizing the square sum of some predefined error measures. If a data set is a set of measurement points in 2-D/3-D space, the most natural error measure is the shortest distance between the model feature and the measurement point. This is because the measurement point is a probable observation of the nearest object point to the measurement point. The least-squares model fitting with this error measure is called orthogonal distance fitting (ODF). The square sum of the error distances can be described in two ways: the distance-based cost function and the coordinate-based cost function. The numerical methods minimizing these two cost functions are respectively called the distance-based algorithm and the coordinate-based algorithm.The ODF task has two sub-problems: not only do the model parameters have to be found but also the shortest distance points on a model feature from each given point. In general, the shortest distance point is by nature nonlinear to the model parameters. Thus, the ODF problem is inherently a nonlinear minimization problem solved through iteration. There are two minimization strategies for solving the ODF problems through iteration. The total method simultaneously finds both the model parameters and the shortest distance points, whilst the variable-separation method finds them alternately using a nested iteration scheme. Four combinations of the two numerical methods and the two minimization strategies can be applied. The combination of the distance-based algorithm with the total method results in an underdetermined linear equation system for iteration and is thus impractical for solving ODF problems. As a result, three algorithmic approaches for solving ODF problems are realistic:-Algorithm I: combination of the coordinate-based algorithm with the total method -Algorithm II: combination of the distance-based algorithm with the variableseparation method -Algorithm III: combination of the coordinate-based algorithm with the variableseparation method.This book presents the ODF Algorithms I-III for implicit and parametric curves/ surfaces in 2-D/3-D space possessing the following algorithmic features:-Estimation of the model parameters minimizing the square sum of the shortest distances between a model feature and the given points in 2-D/3-D space -General application to parametric and implicit curves/surfaces VIII Preface -Estimation of the model parameters in terms of form, position, and rotation -General implementation of the distance-based and coordinate-based algorithms -General implementation of the total and variable-separation methods -Solving ODF problems with additional constraints -Provision of parameter-testing possibilities -Robust and fast convergence -Low computing costs and memory space usage -Low implementation costs for a new model feature -Automatic determination of the initial parameter values for iteration.Chapter 1 first reviews the mathematical descriptions of curves and surfaces in s...

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“…5.2 [11]). Note that the determination of the minimum distance points is the time-consuming element in the overall procedure.…”

Section: Domain Volume For Measurement Pointsmentioning

confidence: 96%

Section: Object Reconstruction From Unordered Point Cloudmentioning

confidence: 99%

Section: Semi-automatic Object Recognitionmentioning

confidence: 99%

“…5.1) was used. The procedure starts by picking one or two seed points in a point cloud and choosing a low-level model type [9], [11]. If necessary, the model type can be updated, e.g.…”

Section: Segmentation Outlier Elimination and Model Fittingmentioning

confidence: 99%

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Least Squares Orthogonal Distance Fitting of Curves and Surfaces in Space

Ahn¹

2004

Lecture Notes in Computer Science

102

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“…The coating edge is visible in the middle of the point cloud. By fitting a plane [8] the deviation to this plane can be measured and color coded visualized (see Figure 7, the scale values are in mm). When this fitting method is used for measuring deviations the above explained definition, measurement and calculation of a zero point can be avoided, as long as the conveyor and the coating area are both in the measuring field.…”

Section: Generating Point Cloudmentioning

confidence: 99%

Automated inline visual inspection and 3D measuring in electrode manufacturing

Frommknecht

Schmauder

Boonen

et al. 2019

Optical Measurement Systems for Industrial Inspection XI

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Electrode manufacturing is one of the most critical processes within the energy storage production chain, as the electrodes coating and drying quality determines the later performance of the energy storage system in large part. Critical quality losses within the electrode coating are often optical visible, but cannot be automatically and inline inspected. Often small defects like agglomerates, capillary cracks or pinholes in the carbon or metal oxide based layers have to be detected at high web speeds. In the work described in this paper it was examined which kind of features and defects can be recognized by two optical methods: visual inspection and 3D measuring.A visual camera in conjunction with a white illumination and a 3D data laser line system has been used. Image processing algorithms are applied on the scanned data to detect pinholes and agglomerates. Also thickness of coating, gradient of the coating edge and form anomalies can be determined out of the produced data. Reliable evaluation of pinholes even of small sizes has been proofed. Particle agglomerations are more challenging. To achieve good enough data for 3D evaluation standard sensors are often insufficient, due to the necessary resolution and the production speed that requires high scanning frequency.In order to provide an automated, digitalized production and inspection system, the devices were coupled to a central experiment management system. Their operation is controlled non-locally in the cloud and synchronized with the execution of experiments. The acquired raw data is stored for later evaluation and long-term archiving.

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