Seeded region growing: an extensive and comparative study

Fan, Jianping; Zeng, Guihua; Body, Mathurin; Hacid, Mohand-Saïd

doi:10.1016/j.patrec.2004.10.010

Cited by 215 publications

(97 citation statements)

References 35 publications

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“…We propose an optimal method that can obtain a planar patch model from a 3D point set in O(log n). This method is based on automatic seed selection methods Shih & Cheng (2005) Fan et al (2005). The idea consists in performing a selection of the most representative points in the whole 3D scene.…”

Section: Features Extraction Methodsmentioning

confidence: 99%

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

et al. 2012

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Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D features extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.

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Section: Features Extraction Methodsmentioning

confidence: 99%

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

et al. 2012

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“…Object tracking has previously been performed mostly for color/gray-scale image sequences [1,7,8,9,10]. However, depth images pose different challenges to the tracking algorithm than color/gray-scale images.…”

Section: Related Workmentioning

confidence: 99%

Robust surface tracking in range image sequences

Husain

Dellen

Torras

2014

Digital Signal Processing

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A novel robust method for surface tracking in range-image sequences is presented which combines a clustering method based on surface models with a particle-filter-based 2-D affine-motion estimator. Segmented regions obtained at previous time steps are used to create seed areas by comparing measured depth values with those obtained from surface-model fitting. The seed areas are further refined using a motion-probability region estimated by the particlefilter-based tracker through prediction of future states. This helps resolving ambiguities that arise when surfaces belonging to different objects are in physical contact with each other, for example during hand-object manipulations. Region growing allows recovering the complete segment area. The obtained segmented regions are then used to improve the predictions of the tracker for the next frame. The algorithm runs in quasi real-time and uses on-line learning, eliminating the need to have a priori knowledge about the surface being tracked. We apply the method to in-house depth videos acquired with both time-of-flight and structured-light sensors, demonstrating object tracking in real-world scenarios, and we compare the results with those of an ICP-based tracker.

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“…In the first one, we select a subset of points by means of classical computer vision seed selection methods Fan et al (2005) Shih and Cheng (2005). Seeds are spread along the surfaces of the scene.…”

Section: D Scene Modelizationmentioning

confidence: 99%

A robust and fast method for 6DoF motion estimation from generalized 3D data

Viejo

Cazorla

2013

Auton Robot

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Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six Degrees of Freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.

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Seeded region growing: an extensive and comparative study

Cited by 215 publications

References 35 publications

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Robust surface tracking in range image sequences

A robust and fast method for 6DoF motion estimation from generalized 3D data

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