利用散斑获取深度图 -

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利用散斑获取深度图

doi:10.3788/CO.20160906.0633

长安大学信息工程学院, 陕西西安 710064

基金项目:

中央高校基本科研业务费资助项目（自然科学类）No.310824152009

详细信息

通讯作者:
张绍阳(1971-),男,山西襄汾人,博士后,教授,硕士生导师,主要从事交通信息工程、软件技术及理论、模式识别等方面的研究。E-mail:zhsy@chd.edu.cn

中图分类号:TP391.4
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出版历程
- 收稿日期:2016-06-22
- 修回日期:2016-07-18
- 刊出日期:2016-12-01

Depth image acquisition using laser speckle

Information Engineering College, Changan University, Xi'an 710018, China

Funds:

Supported by Basic Scientific Research Projects(Natural Sciences) of Central Universities of ChinaNo.310824152009

More Information

Corresponding author:E-mail:zhsy@chd.edu.cn

摘要

摘要:相对于普通灰度和彩色二维图像，深度图像可以得到物体的三维信息，使视觉识别和人机交互更加智能。国内外目前还没有低成本、公开的实时获取高质量深度图的方法。本文在对散斑图获取深度图原理研究的基础上，采取散斑的方式，运用块匹配的方法给出了一种大范围深度图的获取方法。首先，从原理上验证了块匹配方法的可行性；然后，分别从理论和实验两个方面对深度图的计算公式进行了推导和验证；再次，对深度图恢复过程进行了详细叙述，包括散斑图像的预处理和块匹配的过程；最后，给出了运用该块匹配方法得到的实验数据。实验结果表明，本文方法在物体距离相机50 cm左右时精度可以达到5 mm，200 cm时精度可以达到5 cm，可以满足室内大部分对象的识别要求。
- 散斑/
- 深度图/
- 块匹配/
- 块匹配快速算法
Abstract:Compared with the ordinary 2D gray and color images, 3D information of target can be obtained by depth image, making the visual identification and human-computer interaction more intelligent. There is no open and real-time method to obtain high quality depth image at low cost at home and abroad. In this paper, based on the principle research on obtaining the depth image by laser speckle, a way to obtain wide range of depth image is presented by laser speckle and the method of template matching. Firstly, the feasibility of the method of template matching is proved in this paper. Then, the formula of depth image is calculated and verified from both theory and experiments. After that, the process of recovering depth image is described in detail, including pretreatment of speckle image and the process of block matching. Finally, the experimental data obtained by this template matching method are given. The experimental results show that the precision can reach 5 mm when the objects is 50 cm far from the camera and 5 cm when the object is 200 cm far from the camera by using the method proposed in this paper, which can satisfy the reguirements of most indoor objects' recognition.
- laser speckle/
- depth image/
- template matching/
- fast algorithm of template matching

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图 1散斑图获取示意图

Figure 1.Schematic diagram of laser speckle′s acquisition

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图 2同一平面点(768,450) 与整幅图互相关系数三维图

Figure 2.Cross-correlation coefficient′s 3D graph of the same plane′s point(768,450) and the whole image

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图 3不同平面点(768,450) 与整幅图互相关相关系数三维图

Figure 3.Cross-correlation coefficient′s 3D graph of the different plane′s point (768,450) and the whole image

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图 4光路几何图

Figure 4.Geometric graph of light path

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图 5实际测量到的深度与像素横向偏移量关系曲线

Figure 5.Curve of measured depth vs. pixel′s crosswise offset

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图 6公式计算到的深度与像素横向偏移量关系曲线

Figure 6.Curve of depth computed with formula vs. pixel′s crosswise offset

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图 7局部自适应二值化图

Figure 7.Local adaptive binary image

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图 8剔除后的散斑二值化图

Figure 8.Speckle binary image after excluding isolated points

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图 9单点散斑图

Figure 9.Single points speckle image

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图 10参考图与测试图异或区域示意图

Figure 10.Schematic diagram of reference image and test image′s xor area

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图 11匹配到的异或图

Figure 11.Matched Xor image

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图 12未匹配到的异或图

Figure 12.Unmatched Xor image

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图 13不同距离亮斑图

Figure 13.Bright speckle image at different distances

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图 14测试散斑图和计算出的深度图

Figure 14.Test speckle image and the computed depth image

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图 15斜面深度图

Figure 15.Depth image with inclined plane

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图 16斜面中一行的曲线图

Figure 16.Curve of graphone row in inclined plane

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表 1深度与横向偏移量关系

Table 1.Relation of depth and crosswise offset

深度/m	横向偏移/pixel	纵向偏移/pixel	深度/m	横向偏移/pixel	纵向偏移/pixel	深度/m	横向偏移/pixel	纵向偏移/pixel	深度/m	横向偏移/pixel	纵向偏移/pixel
0.5	58.87	-0.82	0.9	5.99	0.26	1.3	-13.18	1.10	1.7	-25.17	1.16
0.6	39.22	-0.42	1.0	0	0	1.4	-16.52	1.36	1.8	-24.97	0.86
0.7	24.62	0	1.1	-3.69	0.43	1.5	-19.88	1.49	1.9	-28.23	0
0.8	14.84	0.17	1.2	-8.80	0.75	1.6	-23.89	1.68	2.0	-28.71	0.75

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表 2实际深度与计算出的深度对比

Table 2.Comparison of real depth and computed depth

实际深度/cm	测量深度/cm	实际深度/cm	测量深度/cm	实际深度/cm	测量深度/cm	实际深度/cm	测量深度/cm
50	49.5	90	88.07	130	128.48	170	172.09
60	58.27	100	100	140	140.72	180	183.23
70	69.00	110	109.09	15	149.97	190	192.00
80	79.12	120	119.97	160	161.04	200	205.94

下载: 导出CSV

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