Edge Detection in Range Images Based on Scan Line Approximation

Jiang, Xiaoyi; Bunke, Horst

doi:10.1006/cviu.1998.0715

Cited by 184 publications

(167 citation statements)

References 32 publications

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“…Surface-matching by harmonic maps have been used by Zhang and Hebert [89]. Edge detection has been used by Jiang and Bunke [44]. Cooper et al [16] studied information theoretic measures, entropy and mutual information, as performance metrics for object recognition.…”

Section: Object Identificationmentioning

confidence: 99%

Construction object identification from LADAR scans:

Gilsinn¹,

Witzgall²,

Cheok³

et al. 2005

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Laser Scanning devices (a.k.a. LADAR for Laser Detection and Ranging) are primarily used in construction projects to capture as-built data for documentation and re-engineering. These systems can rapidly generate unstructured point clouds of scene data within the field of view and line-of-sight of the LADAR. The Construction Metrology and Automation Group (CMAG) at NIST is interested in using these high-resolution scanned data to identify and locate construction objects of interest. The test operation is large-scale pick-and-place of structural steel, where the data from the scans are used to identify the general pose, position and orientation of a target object for initial positioning of a robotic crane. Close-in control of the crane using high framerate flash LADAR is a separate but related area of CMAG research.This initial study describes an experiment in which an I-beam on a concrete floor surface is scanned, and the resulting point cloud data are used to calculate its pose. Multiple scans of two sizes of I-beams were taken with the I-beam placed at various angles relative to the scanner. Two approaches for segmenting potential target objects are described: binning and triangular irregular networks (TINs). Using the method of principal components analysis, the pose of potential target objects is determined. Bounding boxes are then formed around these objects and compared to an ideal bounding box generated from the known geometric specifications of the Ibeam of interest. A measure of best fit is used to determine which scanned object most closely fits the bounding box of the ideal I-beam. A separate laser-based site measurement system (SMS) was used to measure points on the I-beams to form reference data for estimating the closeness of fit of computed pose of the I-beam to measured pose of the I-beam. Three spheres were located in the scan field as a means of registering the scan and SMS axes to one another.

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Section: Object Identificationmentioning

confidence: 99%

Construction object identification from LADAR scans:

Gilsinn¹,

Witzgall²,

Cheok³

et al. 2005

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“…Finally, the proposed approach is applied to real range images, this last test include several real range images from Vision lab data base at the University of South Florida (K2T structured light sensor). Edge points were computed by means of two edge-based range image segmentation techniques ( [2] and [6]), in addition by using different approximation errors-approximation error defines the density of edge points.…”

Section: Resultsmentioning

confidence: 99%

“…[2] brought forward an adaptive approach that extracts closed contour by applying a process of hypotheses generation and verification. This algorithm is based on the consideration that any contour gap can be closed by dilating the input edge map.…”

Section: Contour Closure Algorithmmentioning

confidence: 99%

“…Edge points were computed by using the code presented in [2] and [6]; the difference between them is that in [2], not only rows and columns are considered but also diagonals. Edge points were computed at different approximation errors.…”

Section: Real Range Imagesmentioning

confidence: 99%

“…Classically, they were inspired from the 2D image processing field; hence, some of the proposed 3D contour closure approaches have been based on the use of morphological operators (e.g., [2], [3], [4]). Other approaches try to link edge points according to local measures of continuity and smoothness, with no a priori information about the object shape.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Closed Contour Extraction from Range Image’s Edge Points

Sappa¹

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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-This paper presents an improvement over a previous contour closure algorithm. Assuming that edge points are given as input, the proposed approach consists of two steps. Similarly than the previous approach, the minimum spanning tree of a partially connected graph is initially computed. Then, a morphological filter removes noisy links and finally open contours are closed by minimizing a linking cost function. Advantages of the proposed technique lie in the lack of user defined thresholds and non-dependency of edge point density. Experimental results with synthetic and real range images are presented showing encouraging results with uniform and non-uniform edge points' distribution.

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References

2016

Dictionary of Computer Vision and Image Processing

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Edge Detection in Range Images Based on Scan Line Approximation

Cited by 184 publications

References 32 publications

Construction object identification from LADAR scans:

Construction object identification from LADAR scans:

Efficient Closed Contour Extraction from Range Image’s Edge Points

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