基于<b>WCGAN</b>的矿物浮选泡沫图像光照不变颜色提取

刘金平; 何捷舟; 唐朝晖; 谢永芳; 马天雨

doi:10.16383/j.aas.c190330

基于WCGAN的矿物浮选泡沫图像光照不变颜色提取

doi: 10.16383/j.aas.c190330

1.
湖南师范大学智能计算与语言信息处理湖南省重点实验室长沙 410081
2.
中南大学自动化学院长沙 410083
3.
湖南师范大学物理与电子科学学院长沙 410081

基金项目: 国家自然科学基金(61971188, 61771492), 国家杰出青年科学基金(61725306), 国家自然科学基金−广东联合基金重点项目(U1701261), 湖南省自然科学基金(2018JJ3349), 湖南省研究生科研创新项目(CX2018B312, CX20190415)资助

详细信息

作者简介:
刘金平：湖南师范大学信息科学与工程学院副教授. 主要研究方向为智能信息处理. 本文通信作者.E-mail: ljp202518@163.com

何捷舟：湖南师范大学信息科学与工程学院硕士研究生. 主要研究方向为计算机视觉和模式识别.E-mail: hdc@smail.hunnu.edu.cn

唐朝晖：中南大学自动化学院教授. 2005年至2006年任德国杜伊斯堡−埃森大学访问学者. 主要研究方向为信号处理和工业过程故障诊断. E-mail: zhtang@csu.edu.cn

谢永芳：中南大学自动化学院教授. 主要研究方向为复杂工业过程的建模与控制, 分散鲁棒控制, 故障诊断. E-mail: yfxie@csu.edu.cn

马天雨：博士, 湖南师范大学物理与电子学院讲师. 主要研究方向为复杂工业过程建模及优化控制.E-mail: mty@hunnu.edu.cn

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出版历程
- 收稿日期: 2019-05-05
- 录用日期: 2019-09-02
- 网络出版日期: 2022-08-08
- 刊出日期: 2022-09-16

WCGAN-based Illumination-invariant Color Measuring of Mineral Flotation Froth Images

1.
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081
2.
School of Automation, Central South University, Changsha 410083
3.
School of Physics and Electronics, Hunan Normal University, Changsha 410081

Funds: Supported by National Natural Science Foundation of China (61971188, 61771492), National Science Fund for Distinguished Yong Scholars (61725306), Joint Found of National Natural Science Foundation of China and Guangdong Provincial Government (U1701261), Hunan Natural Science Fund (2018JJ3349), and Hunan Postgraduate Research Innovation Project (CX2018B312, CX20190415)

More Information

Author Bio:
LIU Jin-Ping　Associate professor at the College of Information Science and Engineering, Hunan Normal University. His research interest covers digital signal processing and pattern recognition. Corresponding author of this paper

HE Jie-Zhou　Master student at the College of Information Science and Engineering, Hunan Normal University. His research interest covers computer vision and pattern recognition

TANG Zhao-Hui　Professor at the School of Automation, Central South University. He was a visiting scholar at the University of Duisburg-Essen, Germany from 2005 to 2006. His research interest covers signal processing and industrial process fault diagnosis

XIE Yong-Fang　Professor at the School of Automation, Central South University. His research interest covers modeling and control of complex industrial processes, decentralized robust control, and fault diagnosis

MA Tian-Yu　Ph.D., lecturer at the College of Physics and Electronics, Hunan Normal University. His research interest covers complex industrial process modeling and optimal control

摘要

摘要: 浮选泡沫表面颜色是选矿生产指标(精矿品位)最为快速便捷的直接指示器. 然而, 泡沫图像信号因受多种可变光照的交叉干扰而不可避免存在严重色偏, 导致浮选指标难以准确评估. 本文将传统的基于光照估计的图像颜色恒常问题转换为一种结构保持的图到图颜色(风格)转移问题, 提出一种基于Wasserstein距离的循环生成对抗网络(Wasserstein distance-based cycle generative adversarial network, WCGAN)用于泡沫图像光照不变颜色特征在线监测. 在标准颜色恒常数据集和实际的工业铝土矿浮选过程进行实验验证, 结果表明, WCGAN能有效实现各种未知光照条件下(色偏)图像到基准光照条件下的颜色转换, 转换速度快且具有模型在线更新功能. 与传统的基于生成对抗学习的颜色转换模型相比, WCGAN能更好地保持泡沫图像的轮廓和表面纹理等结构信息, 为基于机器视觉的矿物浮选过程生产指标的在线监测提供了有效的客观评价信息.
- 浮选泡沫图像 /
- 循环生成对抗网络 /
- 光照不变颜色特征 /
- Wasserstein距离 /
- 结构保持
Abstract: The surface color of flotation froth can be referred as an instant and direct production index (e.g., concentrate grade) of mineral flotation process. However, serious color deviations will inevitably exist in the collected froth images, due to the adverse interference of varying multi-source illuminations, resulting in great difficulties in achieving accurate evaluation results of production indexes. This paper formulates the illumination estimation task in traditional color constancy as a structure-preserved image-to-image color translation task. It presents a Wasserstein distance-based cycle generative adversarial network (WCGAN) for the illumination-invariant froth color feature measuring. Extensive validation and comparative experiments on benchmark color constancy datasets and an industrial flotation process demonstrate that WCGAN can effectively realize the color translation of froth images from various unknown illuminations to a canonical illumination, which has the merits of fast translation and online model updating. Compared with the traditional generative adversarial networks-based color translation models, WCGAN can maintain the structural features invariance, e.g., geometric and texture structures, of original images more effectively, providing effective and objective evaluation information for the on-line monitoring of production indices of mineral flotation process.
- Flotation froth image /
- cycle generative adversarial networks /
- illumination-invariant color characteristics /
- Wasserstein distance /
- structure preserving

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图 1 泡沫图像光照转换思想

Fig. 1 Scheme of the color translation of froth images

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图 2 CycleGAN结构图

Fig. 2 CycleGAN structure

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图 3 WCGAN的生成器结构

Fig. 3 Generator structure of WCGAN

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图 4 图像颜色校正结果

Fig. 4 Image color correction results

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图 5 铝土矿浮选回路

Fig. 5 Bauxite flotation circuit

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图 6 基准光照泡沫图像及其Lab颜色分布

Fig. 6 Reference light froth image and its Lab color distribution

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图 7 浮选泡沫图像颜色校正结果

Fig. 7 Color correction result of flotation froth image

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图 8 泡沫图像颜色特征与A/S间相关性 ((a1)和(a2)分别代表校正后和校正前H均值与A/S间的相关性;(b1)和(b2)分别代表校正后和校正前a通道的标准差与A/S的相关性; (c1)和(c2)分别代表校正后和校正前的归一化R通道均值与A/S之间的相关性)

Fig. 8 The correlation between color characteristics of froth images and A/S ((a1) and (a2) represent the correlation between H-means and A/S after correction and before correction; (b1) and (b2) represent the correlation between standard deviation of a-channel and A/S after correction and before correction; (c1) and (c2) represent the correlation between normalized R-channel mean and A/S after correction and before correction, respectively)

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图 9 基于泡沫图像颜色特征的精矿品位预测

Fig. 9 Prediction of concentrate grade based on color characteristics of foam images

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表 1 基于统计量的颜色恒常方法在Gehler-Shi 568 data 上的对比结果

Table 1 Comparison of statistics-based color constancy methods on Gehler-Shi 568 data

方法	色度误差			角度误差			测试时间 (s)
方法	Median	Max	RMS	Mean	Max	RMS	测试时间 (s)
Gray-Edge^[30]	0.62	1.35	0.73	6.3	10.4	6.5	0.9
MAX-RGB^[28]	1.17	2.55	1.26	9.9	18.6	10.3	0.7
Gray-World^[29]	0.78	1.47	0.88	7.6	17.9	8.4	0.8
White-patch^[31]	0.73	1.56	0.81	7.5	14.7	8.3	0.9

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表 2 基于机器学习的颜色恒常方法在Gehler-Shi 568 data上的对比结果

Table 2 Comparison of machine learning-based color constancy methods on Gehler-Shi 568 data

方法	SSIM	色度误差			角度误差			训练时间 (s)	测试时间 (s)
方法	SSIM	Median	Max	RMS	Mean	Max	RMS	训练时间 (s)	测试时间 (s)
FC4^[13]	0.8576	0.57	1.39	0.65	4.7	11.3	5.6	1.7	0.9
Neural Gray^[33]	0.9166	0.69	1.92	0.77	5.7	13.4	6.5	1.4	0.5
Based-SVR^[34]	0.8945	0.61	1.88	0.70	5.4	12.6	6.3	1.6	1.2
CycleGAN^[35]	0.6918	0.98	3.11	1.07	6.3	16.5	7.4	3.0	0.12
WD + CycleGAN	0.8399	0.76	1.84	0.69	5.1	14.3	5.9	3.0	0.12
WCGAN	0.9897	0.42	1.31	0.50	4.3	10.5	5.4	1.5	0.06

下载: 导出CSV

表 3 基于统计量的颜色恒常方法在SFU 321 lab images上的对比结果

Table 3 Comparison of statistics-based color constancy methods on SFU 321 lab images

方法	色度误差			角度误差			测试时间 (s)
方法	Median	Max	RMS	Mean	Max	RMS	测试时间 (s)
Gray-Edge^[30]	0.54	1.26	0.62	5.9	12.7	6.8	0.9
MAX-RGB^[28]	1.16	2.46	1.24	10.5	17.6	11.4	0.7
Gray-World^[29]	0.74	1.43	0.83	7.9	18.2	8.7	0.8
White-patch^[31]	0.64	1.49	0.72	7.1	15.3	7.9	0.9

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表 4 基于机器学习的颜色恒常方法在SFU 321 lab images上的对比结果

Table 4 Comparison of machine learning-based color constancy methods on SFU 321 lab images

方法	SSIM	色度误差			角度误差			训练时间 (s)	测试时间 (s)
方法	SSIM	Median	Max	RMS	Mean	Max	RMS	训练时间 (s)	测试时间 (s)
FC4^[13]	0.8791	0.61	1.45	0.69	5.2	9.0	6.0	1.1	0.7
Neural Gray^[33]	0.9286	0.71	1.87	0.80	6.4	12.1	7.3	0.9	0.4
Based-SVR^[34]	0.9139	0.63	1.84	0.72	5.8	12.1	6.5	1.3	0.9
CycleGAN^[35]	0.7347	0.84	2.11	0.92	6.2	15.7	7.9	2.7	0.09
WD+CycleGAN	0.9145	0.70	1.75	0.66	4.7	13.9	6.9	2.7	0.09
WCGAN	0.9936	0.39	1.28	0.45	3.1	12.2	4.1	1.2	0.05

下载: 导出CSV

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