A Multi-Object Image Segmentation Algorithm Based on Local Features

Fig. 4. Segmentation results obtained by different algorithms for the scene No. 5. (a) Algorithm in Ref. [5]; (b) FCN algorithm; (c) proposed algorithm with preprocessing; (d) proposed algorithm without preprocessing

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Fig. 5. Segmentation results obtained by different algorithms for the scene No. 8. (a) Algorithm in Ref. [5]; (b) FCN algorithm; (c) proposed algorithm with preprocessing; (d) proposed algorithm without preprocessing

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Table 1. Comparison of matching points between adaptive threshold and fixed threshold
View table
Table 1. Comparison of matching points between adaptive threshold and fixed threshold
Parameter Adaptivethreshold Fixed threshold
θ^S=150 θ^S=200 θ^S=250
Number of feature points 50 40 55 89
Number of mismatching feature points 1 2 3 13
Mismatching rate /% 2.0 5.0 5.5 14.6

Table 2. Comparison of mismatching rate between the proposed SIFT algorithm and the original SIFT algorithm
View table
Table 2. Comparison of mismatching rate between the proposed SIFT algorithm and the original SIFT algorithm
Scene Mismatching rate /%
Original SIFTalgorithm Proposed SIFTalgorithm
1 18.9 1.5
2 10.0 0
3 9.3 1.7
4 9.7 0
5 14.3 2.0
6 10.4 1.9
7 22.5 1.2
8 19.8 2.7
Average 13.2 1.4

Table 3. Comparison of segmentation errors among FCN algorithm, the algorithm in Ref. [5] and the proposed algorithms

View table

Table 3. Comparison of segmentation errors among FCN algorithm, the algorithm in Ref. [5] and the proposed algorithms

Scene		1	2	3	4	5	6	7	8	Average
Proposedalgorithm (withpreprocessing)	R_O /%	3.40	4.38	7.49	6.47	9.84	9.86	9.18	9.28	7.49
	R_U /%	0	0	0	0	0	0	0	0	0
	R_E /%	3.40	4.38	7.49	6.47	9.84	9.86	9.18	9.28	7.49
	Proposedalgorithm(withoutpreprocessing)	R_O /%	3.40	4.31	7.49	6.17	8.95	9.83	7.87	8.09	7.01
R_U /%		0	1.70	0	0.85	1.29	0.26	2.65	1.20	0.99
R_E /%		3.40	6.11	7.49	7.08	10.37	10.12	10.80	9.40	8.10
FCNalgorithm	R_O /%	2.42	1.17	3.53	4.04	5.94	5.72	7.70	4.70	4.40
	R_U /%	1.12	4.36	2.95	2.08	3.34	2.54	1.82	3.81	2.74
	R_E /%	3.58	6.79	6.68	6.25	9.48	8.47	9.69	8.85	7.47
Algorithmin Ref. [5]	R_O /%	18.19	25.00	20.39	23.26	14.26	31.88	14.11	11.77	19.86
	R_U /%	0	23.12	15.79	13.04	10.20	26.67	3.23	15.44	13.44
	R_E /%	18.19	62.59	42.96	41.74	27.25	79.83	17.92	32.18	40.33

Table 4. Runtime of each stage of the proposed algorithm (unit: s)
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Table 4. Runtime of each stage of the proposed algorithm (unit: s)
Algorithm Preprocessing Stereomatching Region refinement basedon depth information SIFTmatching Mean shift Total
Withpreprocessing 0.38 0.06 0.06 0.49 0.02 1.01
Withoutpreprocessing 0.06 0.06 1.51 0.02 1.65

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Lin Wang, Qiang Liu. A Multi-Object Image Segmentation Algorithm Based on Local Features[J]. Laser & Optoelectronics Progress, 2018, 55(6): 061002

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Paper Information

Category: Image Processing

Received: Nov. 2, 2017

Accepted: --

Published Online: Sep. 11, 2018

The Author Email: Qiang Liu (qiangliu@tju.edu.cn)

DOI:10.3788/LOP55.061002

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison of matching points between adaptive threshold and fixed threshold

Table 1. Comparison of matching points between adaptive threshold and fixed threshold

Table 2. Comparison of mismatching rate between the proposed SIFT algorithm and the original SIFT algorithm

Table 2. Comparison of mismatching rate between the proposed SIFT algorithm and the original SIFT algorithm

Table 3. Comparison of segmentation errors among FCN algorithm, the algorithm in Ref. [5] and the proposed algorithms

Table 3. Comparison of segmentation errors among FCN algorithm, the algorithm in Ref. [5] and the proposed algorithms

Table 4. Runtime of each stage of the proposed algorithm (unit: s)

Table 4. Runtime of each stage of the proposed algorithm (unit: s)