Some further results of experimental comparison of range image segmentation algorithms

Jiang, Xiaoyi; Bowyer, Kevin W.; Morioka, Yushi; Hiura, Shinsaku; Sato, K.; Ishikawa, Seiji; Bock, Mary Ellen; Guerra, Concettina; Loke, R.E.; Buf, J. M. H. du

doi:10.1109/icpr.2000.903055

Cited by 44 publications

(30 citation statements)

References 3 publications

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“…A thorough review of the latest algorithms is missing in the literature. However, the evaluation done in Hoover et al (1996) and Jiang et al (2000a) is still of actuality. Furthermore, a detailed description of various range image segmentation methods can be found in Jiang and Bunke (1997).…”

Section: Plane Extractionmentioning

confidence: 99%

Results on Range Image Segmentation for Service Robots

Gächter

Nguyen

Siegwart

2006

Fourth IEEE International Conference on Computer Vision Systems (ICVS'06)

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This report presents an experimental evaluation of a plane extraction method using various line extraction algorithms. Four different algorithms are chosen, which are well known in mobile robotics and computer vision. Experiments are performed on two sets of 25 range images either obtained by simulation or acquired by a proprietary 3D laser scanner. The segmentation outcome of the simulated range images is measured in terms of an average segment classification ratio. Moreover, the speed of the method is measured to conclude on the suitability for service robot applications.

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Section: Plane Extractionmentioning

confidence: 99%

Results on Range Image Segmentation for Service Robots

Gächter

Nguyen

Siegwart

2006

Fourth IEEE International Conference on Computer Vision Systems (ICVS'06)

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“…Many algorithms have been proposed for single range-image segmentation [11], [26], [27], [28], [29], [30], [31], [32], [33]. Most of these methods use detection of local depth discontinuities to segment the depth data.…”

Section: B Single Range-image Segmentationmentioning

confidence: 99%

“…We report segmentation performance of our method for the ABW and the Perceptron datasets [43] using the evaluation framework of [11] and compare it to various single-rangeimage segmenters [11], [26], [27], [28], [29], [30], [31], [32], [33], [7] (see Table I). For these datasets, we first used the provided training sequence (10 samples) to determine the optimal parameter values for ρ, τ 1 and τ 2 .…”

Section: A Comparative Evaluation Of Single Range Image Segmentationmentioning

confidence: 99%

Consistent Depth Video Segmentation Using Adaptive Surface Models

Husain

Dellen

Torras

2015

IEEE Trans. Cybern.

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Abstract-We propose a new approach for the segmentation of 3-D point clouds into geometric surfaces using adaptive surface models. Starting from an initial configuration, the algorithm converges to a stable segmentation through a new iterative splitand-merge procedure, which includes an adaptive mechanism for the creation and removal of segments. This allows the segmentation to adjust to changing input data along the movie, leading to stable, temporally coherent, and traceable segments. We tested the method on a large variety of data acquired with different range imaging devices, including a structured-light sensor and a time-of-flight camera, and successfully segmented the videos into surface segments. We further demonstrated the feasibility of the approach using quantitative evaluations based on ground-truth data.

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“…The literature of range image segmentation is vast and comprises works that deal with comparisons among different algorithms (see [28,30,42]). Besides segmentation into parts using geometric characterizations, we are also interested in recovering primitives [10,23,33,41,57] as well as in analyzing the interconnections among the parts [19].…”

Section: Low-level Processing-3d Segmentationmentioning

confidence: 99%

Efficient Search and Verification for Function Based Classification from Real Range Images

Shimshoni

Rivlin

Soldea

2006

18th International Conference on Pattern Recognition (ICPR'06)

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In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about 150 real raw range images of object instances from 10 classes.

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Some further results of experimental comparison of range image segmentation algorithms

Cited by 44 publications

References 3 publications

Results on Range Image Segmentation for Service Robots

Results on Range Image Segmentation for Service Robots

Consistent Depth Video Segmentation Using Adaptive Surface Models

Efficient Search and Verification for Function Based Classification from Real Range Images

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