A Fast Segmenting Method for Scenes with Stacked Plate-Shaped Objects

Rongrong Lu; Feng Zhu; Qingxiao Wu; Yunge Cui; Yanzi Kong; Foji Chen

doi:10.3788/AOS201939.0412003

Acta Optica Sinica, Volume. 39, Issue 4, 0412003(2019)

A Fast Segmenting Method for Scenes with Stacked Plate-Shaped Objects

Rongrong Lu^1,2,3,4,5, Feng Zhu^1,2,4,5、*, Qingxiao Wu^1,2,4,5, Yunge Cui^1,2,3,4,5, Yanzi Kong^1,2,3,4,5, and Foji Chen^1,2,3,4,5

Author Affiliations

¹ Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

² Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

³ University of Chinese Academy of Sciences, Beijing 100049, China

⁴ Key Laboratory of Opto-Electronic Information Processing, Shenyang, Liaoning 110016, China

⁵ Key Laboratory of Image Understanding and Computer Vision, Shenyang, Liaoning 110016, China

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Figures & Tables(13)

Fig. 1. Segmentation results obtained with two algorithms. (a) 3D point cloud (top view); (b) segmentation result obtained with original RANSAC algorithm; (c) segmentation result obtained with modified algorithm

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Fig. 2. Results obtained before and after region merging. (a) Connected planar regions; (b) merged result obtained with glue algorithm

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Fig. 3. Intermediate results obtained with proposed algorithm. (a) Connected planar regions; (b) merged result obtained with glue algorithm; (c) binary image corresponding to the red connected region; (d) binary image after erosion; (e) final segmentation result

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Fig. 4. Plate-shaped objects and data acquisition platform. (a) Six types of plate-shaped objects; (b) simple scene; (c) complex scene

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Fig. 5. An illustration of depth image and its corresponding three-dimensional point cloud. (a) Depth image; (b) three-dimensional point cloud (top view); (c) amplified three-dimensional point cloud

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Fig. 6. Segmentation results obtained with four algorithms. (a) Depth images; (b) Canny edge algorithm; (c) K-means algorithm; (d) RANSAC algorithm; (e) region growing algorithm

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Fig. 7. Comparison between original depth image and labeled images. (a) Original depth image; (b) ground truth (gray image); (c) ground truth (color image)

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Fig. 8. Segmentation results obtained with proposed algorithm on twenty simple scenes

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Fig. 9. Segmentation results obtained with two algorithms on S6, S8 and S15 scenes.(a) Proposed method; (b) region growing algorithm

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Fig. 10. Segmentation results obtained with two algorithms on ten complex scenes. (a) Proposed method; (b) region growing algorithm

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Table 1. Parameter setting of proposed algorithm
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Table 1. Parameter setting of proposed algorithm
N_r τ_ransac /mm N_p N_e N_s α /(°) N_m N_c
1000 3.5 300 50 500 80 1.5M_min 100

Table 2. Segmentation accuracy obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

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Table 2. Segmentation accuracy obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

Scene No.	Segmentation accuracy on simple scenes		Segmentation accuracy on complex scenes
Scene No.	Region growing algorithm	Proposed algorithm	Region growing algorithm	Proposed algorithm
S1	1	0.99	0.95	0.96
S2	0.94	0.94	0.85	0.93
S3	0.97	0.96	0.97	0.97
S4	0.91	0.94	0.92	0.89
S5	0.96	0.94	0.96	0.92
S6	0.82	0.89	0.96	0.96
S7	0.90	0.91	0.93	0.96
S8	0.84	0.99	0.84	0.95
S9	0.99	0.99	0.93	0.94
S10	1	0.99	0.92	0.95
S11	0.97	0.97
S12	1	0.99
S13	1	0.99
S14	1	1
S15	0.94	0.95
S16	1	0.95
S17	1	1
S18	1	1
S19	1	1
S20	0.98	0.98
Average	0.961	0.968	0.923	0.943

Table 3. Average time cost obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

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Table 3. Average time cost obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

Image size /(pixel×pixel)	Average time cost on simple scenes /s		Average time cost on complex scenes /s
Image size /(pixel×pixel)	Region growing algorithm	Proposed algorithm	Region growing algorithm	Proposed algorithm
640×480	11.02	2.14	11.89	3.14
320×240	2.65	0.57	2.59	1.02

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Rongrong Lu, Feng Zhu, Qingxiao Wu, Yunge Cui, Yanzi Kong, Foji Chen. A Fast Segmenting Method for Scenes with Stacked Plate-Shaped Objects[J]. Acta Optica Sinica, 2019, 39(4): 0412003

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Paper Information

Category: Instrumentation, Measurement and Metrology

Received: Nov. 1, 2018

Accepted: Dec. 12, 2018

Published Online: May. 10, 2019

The Author Email:

DOI:10.3788/AOS201939.0412003

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Parameter setting of proposed algorithm

Table 1. Parameter setting of proposed algorithm

Table 2. Segmentation accuracy obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

Table 2. Segmentation accuracy obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

Table 3. Average time cost obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes

Table 3. Average time cost obtained with proposed algorithm and region growing algorithm on twenty simple scenes and ten complex scenes