Table Plane Detection Using Geometrical Constraints  on Depth Images

Hung, Le Van; Vu, Hai; Thủy, Nguyễn Thị Thanh; Lan, Le Thi; Hai, Tran Thi Thanh

doi:10.15625/vap.2015.000204

Cited by 1 publication

(4 citation statements)

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“…The results of the method on the dataset [14] (see Table 3) show that our method obtains a similar accuracy than the methods in [6], [10] on EM1 and EM2 and outperforms these methods on EM3. However, this dataset has more noise than our dataset.…”

Section: Resultsmentioning

confidence: 73%

“…Three methods are mainly different from one another in term of the implementation techniques for the plane segmentations. The first baseline method, PROSAC is deployed (see details in [10]), whereas the second one utilizes the techniques proposed in [6,7]. The proposed method, as described in Sec.…”

Section: Resultsmentioning

confidence: 99%

“…Some parameters are set for the first method as follows: minimum number of points in a plane are 100 points; the thresholds to determine a point belonging the plane in PROSAC algorithm [10] is (t = 0.1) (that means 10cm); the threshold to remove floor plane h is 120 cm. Regarding the second, our method, the parameters are defined as follows: minimum number of points in the region (mininliers) are 1000 points; the threshold for clustering and merging two regions data: angle threshold is 5 degrees, the distance threshold is 0.05 (that means 5cm); the threshold to remove floor plane h is 120 cm.…”

Section: Resultsmentioning

confidence: 99%

“…Although, all these approaches implemented plane segmentation in the scene, they do not address table plane detection in particular. We have proposed an approach for the table plane detection [10] in complex scenes. We used PROSAC [4] algorithm for plane segmentation and some geometrical constraints for table plane extraction in the complex scene.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Table Plane Detection Using Accelerometer Information And Organized Point Cloud Data From Kinect Sensor

Le¹,

Vlaminck²,

Vu³

et al. 2017

JCC

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Table plane detection in the scene is a prerequisite step in developing object-findingaided systems for visually impaired people. In order to determine the table plane in the scene, we have to detect planes in the scene first and then define the table from these detected planes based on the specific characteristics. Although a number of approaches have been proposed for plane segmentation, it still lacks proper table plane detection. In this paper, the authors propose a table plane detection method using information coming from a Microsoft Kinect sensor. The contribution of the paper is threefold. First, for plane detection step, the dedicated down-sampling algorithms to original point cloud thereby representing it as the organized point cloud structure in are applied to get real-time computation. Second, the acceleration information provided by the Kinect sensor is employed to detect the table plane among all detected planes. Finally, three different measures for the evaluation of the table plane detector are defined. The proposed method has been evaluated using a dataset of 10 scenes and published RGB-D dataset which are common contexts in daily activities of visually impaired people. The proposed method outperforms the state-of-the-art method based on PROSAC and obtains a comparable result as a method based on organized point cloud where the frame rate is six times higher.

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Section: Resultsmentioning

confidence: 73%