Roughness Analysis of Sea Surface From Visible Images by Texture

Pan, Hailang; Gao, Peilin; Zhou, Huicheng; Ma, Rong; Yang, Jianguo; Zhang, Xin

doi:10.1109/access.2020.2978638

Cited by 21 publications

(14 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if the 2D imaging system is combined with further image processing that subsequently maps image features to roughness parameters (e.g., , , ), these instruments can be used for quantitative roughness measurement as well [28][29][30][31].…”

Section: B Surface Roughness Measurementmentioning

confidence: 99%

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

et al. 2021

View full text Add to dashboard Cite

In laser-based additive manufacturing (AM) of metal parts from powder bed, information about actual part quality obtained during build is essential for cost-efficient production and high product quality. Reliable and effective monitoring strategies for laser powder bed fusion (LPBF) therefore remain in high demand and are the subject of current research. To address this demand, a novel analysis approach using high dynamic range (HDR) optical imaging in combination with convolutional neural networks (CNN) is proposed for spatially resolved and layer-wise prediction of the surface roughness of LPBF parts. In a further step, the predicted surface roughness maps are used as a feedback signal for a reinforcement learning technique that employs a dynamics model to subsequently identify optimal process parameters under varying and uncertain conditions. The proposed approach ultimately combines the estimation of the local surface roughness based on image texture and model-based reinforcement learning to an in-situ optimization framework for LPBF processes. In addition, the relationship between the layer surface roughness of the part and the overall part density is discussed on the basis of experimental data, which also indicate the applicability of the proposed method in industrial environments. This preliminary study is a first step towards highly adaptive and intelligent machines in the field of automated laser powder bed fusion with the primary goals of reducing production costs and improving the environmental fingerprint as well as print quality.

show abstract

Section: B Surface Roughness Measurementmentioning

confidence: 99%

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Among these, the method of combining the CA and principal component analysis-weight coefficient evaluation plays a key role in the image classification and recognition of cashmere and wool. Step 1: a scanning electron microscope was used to obtain magnified images of cashmere and wool, 20 and the GGCM [21][22][23] was used to extract the texture features of the four sub-images of wavelet transform to construct the original high-dimensional feature data set. 2 Step 2: the original high-dimensional feature data set is analyzed by correlation, 24,25 so that the overlapping features of the cashmere and wool image feature sets are deleted, and a low-dimensional important feature data set is obtained.…”

Section: Overview Of the Cashmere And Wool Fiber Classification Systemmentioning

confidence: 99%

“…1,2,3,17 Feature parameters can not only analyze image gray-scale texture primitives, but also use gray-scale change gradient information to represent texture arrangement information. 29 The GGCM model can comprehensively extract the gray and gradient information of the image, [21][22][23] and the discrete wavelet transform transforms cashmere and wool images into lowfrequency components, high-frequency horizontal edge components, vertical edge components, and diagonal edge component images. 2 We combine the wavelet transform and GGCM to obtain a highdimensional feature data set, and comprehensively consider the image texture characteristics of cashmere and wool.…”

Section: Constructing the Original High-dimensional Feature Data Set ...mentioning

confidence: 99%

Image classification method of cashmere and wool based on the multi-feature selection and random forest method

Zhu

Duan

et al. 2021

Textile Research Journal

View full text Add to dashboard Cite

Cashmere and wool play an important role in the wool industry and textile industry, and suitable features are the key to identifying them. To obtain effective features and improve the accuracy of cashmere and wool classification, the multi-feature selection and random forest method is used to express in this article. Firstly, the gray-gradient co-occurrence matrix model is used for texture feature extraction to construct the original high-dimensional feature data set; secondly, considering that the original feature data set contains a large number of invalid and redundant features, the feature selection algorithm combining correlation analysis and principal component analysis–weight coefficient evaluation is used to obtain important features, independent features, and principal component sensitive features to complement each other; last but not least, the optimized random forest model analyzes the results. The results show that the combination of multi-feature selection subsets and random forest makes the classification accuracy of cashmere and wool more reliable, and the accuracy fluctuates around 90%.

show abstract

“…The amount of pollutant discharges and associated effects on the marine environment are important parameters in evaluating sea water quality. Oil spills can easily be seen but it is difficult, even for an expert, to specify the scope of oil spill at sea [35].…”

Section: Oil Spill Detectionmentioning

confidence: 99%

Roughness Change Analysis of Sea Surface From Visible Images by Fractals

et al. 2020

Self Cite

View full text Add to dashboard Cite

This paper presents an analysis of sea surface roughness from visible images based on fractals features for the first time. We measure fractal dimensions of sea surface images by the methods of box counting, fractional Brownian motion, and area measurement. The empirical values of sea surface roughness are found from measured wind speed and an empirical relation. The correlations between wind speeds (or sea surface roughness) and fractal dimensions are evaluated based on the data from a field experiment. Further, four types of noises with different parameters are introduced to the sea surface images. Then noise suppression performances of six methods are evaluated. Our experiments have demonstrated that our fractal methods and empirical relation between wind speeds and fractal dimensions are effective for analyzing roughness changes of sea surface from visible images. INDEX TERMS Sea surface roughness, oil spill detection, fractal dimensions, box counting, fractional Brownian motions, area measurements.

show abstract

Roughness Analysis of Sea Surface From Visible Images by Texture

Cited by 21 publications

References 20 publications

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

Image classification method of cashmere and wool based on the multi-feature selection and random forest method

Roughness Change Analysis of Sea Surface From Visible Images by Fractals

Contact Info

Product

Resources

About