An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine

Liu, Wei; Tu, Xianming; Jia, Zhenyuan; Wang, Wenqiang; Ma, Xiaoyi; Bi, Xiaodan

doi:10.1007/s00170-013-5048-0

Cited by 42 publications

(22 citation statements)

References 14 publications

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“…Statistical techniques analyze the spatial distribution of gray values by computing local features at each point in the digital image and deriving a set of statistics from the distributions of the local features (Srinivasan and Shobha 2008). Gray level co-occurrence matrices (GLCM) have been used extensively in the pattern recognition sciences for image texture analysis (Davis et al 1979;Gadelmawla 2004;Gui et al 2013;Liu et al 2013). The GLCM features were originally proposed by Haralick et al (1973).…”

Section: Introductionmentioning

confidence: 99%

A Vision System for Surface Homogeneity Analysis of Dough Based on the Grey Level Co‐occurrence Matrix (GLCM) for Optimum Kneading Time Prediction

Alvarado

Hussein

Becker

2015

J Food Process Engineering

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This paper presents an investigation of a methodology for predicting the optimum time to decide dough readiness by using two-dimensional imaging and statistical texture analysis. Based on the master baker experience and the torque curve generated by the dough mixer, the system will be correlated to stop the kneading process with the minimum error on the optimum time or point of maximum dough development (peak consistency). This development takes advantage of the gray level co-occurrence matrix (GLCM) texture analysis for the development of an algorithm to emulate the visual inspection performed by the master bakers, where the homogeneity feature of dough is analyzed during the kneading time. An automatic graphical user interface software for offline and inline analysis was developed for the homogeneity analysis. The programmed algorithm was implemented in many experiments (online and offline) for testing, producing an average error of 33.9 s with respect to average optimal mixing time. PRACTICAL APPLICATIONSThe development of a dough analyzing method, on the basis of an optical surface evaluation, represents an entirely new way to look at the dough properties. The possibility of applying an online dough judgment method without any losses will be given. Simple upgrading of existing production lines can help to prevent erroneous processing and to increase the quality of the end product. Instantaneous state of the art for determining the optimum dough development or dough readiness is the visual and tactile evaluation of the technical staff and laboratory investigations. The expansion of the indirect method by a digital interface-based analytic approach provides a significant improvement in industrial bakery production and a potential direct method for dough analysis.

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Section: Introductionmentioning

confidence: 99%

A Vision System for Surface Homogeneity Analysis of Dough Based on the Grey Level Co‐occurrence Matrix (GLCM) for Optimum Kneading Time Prediction

Alvarado

Hussein

Becker

2015

J Food Process Engineering

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“…Suarez et al [8] have done classification surface roughness of metal parts using image texture features extracted using Gray Level Co-occurrence Matrix (GLCM). Wei et al [9] discussed about the extraction of several micro image features using GLCM and the relationship between surface roughness of deep hole and its image features are derived from support vector machine. Juric et al [10] presented the relationship between surface roughness and surface gloss of paper with print uniformity using GLCM.…”

Section: Introductionmentioning

confidence: 99%

Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Nathan

Thanigaiyarasu²,

Vani

2014

Procedia Engineering

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The Surface roughness and image texture features of milled surfaces are key parameters to study the surface characteristics of end milled AA 6061 alloy. A Machine vision system is employed to capture and store the images of the end milled workpieces.The stylus type instrument is used measure the surface roughness values of various milled workpieces for different cutting conditions such as speed, feed and depth of cut.The Grey Level Cooccurance Matrix [GLCM] is introduced to extract the image texture features of the end milled surfaces. Four Matrices about different sampling orientations are builtup to determine the various image texure features such as contrast, homogenity, correlation and energy. A regression analysis is performed between image texture features and surface roughness values of the machined surfaces. Finally, the relationship between surface roughness and image texture features has been established.

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“…Generally, the measurement techniques of surface roughness can be divided into contact and non-contact methods [9,10]. The most common contact type is the stylus method, which has been used extensively in a lot of systems and performs well [11].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the roughness of the unknown surface can be measured using these image features and the trained prediction model. In the conventional machine vision measurement methods, the roughness correlated features are usually extracted from the gray scale images [10,18,19]. Wei et al presented a Gray Level Co-occurrence Matrix and Support Vector Machine-based method.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Zhang

Liu

Chen

et al. 2019

Sensors

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Due to the limitation of the fixed structures of neighborhood windows, the quality of spatial information obtained from the neighborhood pixels may be affected by noise. In order to compensate this drawback, a robust fuzzy c-means clustering with non-neighborhood spatial information (FCM_NNS) is presented. Through incorporating non-neighborhood spatial information, the robustness performance of the proposed FCM_NNS with respect to the noise can be significantly improved. The results indicate that FCM_NNS is very effective and robust to noisy aliasing images. Moreover, the comparison of other seven roughness indexes indicates that the proposed FCM_NNS-based F index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (R2 = 0.9327 for thirty grinding samples).

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An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine

Cited by 42 publications

References 14 publications

A Vision System for Surface Homogeneity Analysis of Dough Based on the Grey Level Co‐occurrence Matrix (GLCM) for Optimum Kneading Time Prediction

A Vision System for Surface Homogeneity Analysis of Dough Based on the Grey Level Co‐occurrence Matrix (GLCM) for Optimum Kneading Time Prediction

Study On the Relationship between Surface Roughness of AA6061 Alloy End Milling and Image Texture Features of Milled Surface

Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

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