Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture

Lu, Rong; Tian, Gui Yun; Gledhill, Duke; Ward, Steve

doi:10.1364/ao.45.008839

Cited by 75 publications

(47 citation statements)

References 20 publications

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“…the grey-level co-occurrence matrix (GLCM), which deals with spatial relationships of pairs of grey-value pixels (Haralick et al, 1973). Previously, GLCM was applied to retrieving surface roughness from laser speckle images (Lu et al, 2006). Initially, four GLCM features were chosen: contrast, correlation, energy and homogeneity.…”

Section: Methodsmentioning

confidence: 99%

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Incidence of rough and irregular atmospheric ice particles from Small Ice Detector 3 measurements

et al. 2014

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Abstract. The knowledge of properties of ice crystals such as size, shape, concavity and roughness is critical in the context of radiative properties of ice and mixed-phase clouds. Limitations of current cloud probes to measure these properties can be circumvented by acquiring two-dimensional lightscattering patterns instead of particle images. Such patterns were obtained in situ for the first time using the Small Ice Detector 3 (SID-3) probe during several flights in a variety of mid-latitude mixed-phase and cirrus clouds. The patterns are analysed using several measures of pattern texture, selected to reveal the magnitude of particle roughness or complexity. The retrieved roughness is compared to values obtained from a range of well-characterized test particles in the laboratory. It is found that typical in situ roughness corresponds to that found in the rougher subset of the test particles, and sometimes even extends beyond the most extreme values found in the laboratory. In this study we do not differentiate between small-scale, fine surface roughness and large-scale crystal complexity. Instead, we argue that both can have similar manifestations in terms of light-scattering properties and also similar causes. Overall, the in situ data are consistent, with ice particles with highly irregular or rough surfaces being dominant. Similar magnitudes of roughness were found in growth and sublimation zones of cirrus. The roughness was found to be negatively correlated with the halo ratio, but not with other thermodynamic or microphysical properties found in situ. Slightly higher roughness was observed in cirrus forming in clean oceanic air masses than in a continental, polluted one. Overall, the roughness and complexity are expected to lead to increased shortwave cloud reflectivity, in comparison with cirrus composed of more regular, smooth ice crystal shapes. These findings put into question suggestions that climate could be modified through aerosol seeding to reduce cirrus cover and optical depth, as the seeding may result in decreased shortwave reflectivity.

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Section: Methodsmentioning

confidence: 99%

“…It is relevant that the GLCM energy also shows good correlation with roughness in the context of laser speckle from flat surfaces, and is most robust with respect to variation of "the setup configuration, the position, and the orientation of the surface to be measured" (Lu et al, 2006). So only this GLCM feature is reported henceforth.…”

Section: Methodsmentioning

confidence: 99%

Incidence of rough and irregular atmospheric ice particles from Small Ice Detector 3 measurements

et al. 2014

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“…The crystal complexity analysis relies on the grey-level cooccurrence matrix (GLCM) method described in Lu et al (2006). This method was originally developed in the context of quality control of surface treatment processes, and Ulanowski et al (2010) transferred it for the first time to the analysis of SID-3 ice crystal scattering patterns.…”

Section: Scattering Pattern Analysismentioning

confidence: 99%

Cloud chamber experiments on the origin of ice crystal complexity in cirrus clouds

et al. 2016

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Abstract. This study reports on the origin of small-scale ice crystal complexity and its influence on the angular light scattering properties of cirrus clouds. Cloud simulation experiments were conducted at the AIDA (Aerosol Interactions and Dynamics in the Atmosphere) cloud chamber of the Karlsruhe Institute of Technology (KIT). A new experimental procedure was applied to grow and sublimate ice particles at defined super-and subsaturated ice conditions and for temperatures in the −40 to −60 • C range. The experiments were performed for ice clouds generated via homogeneous and heterogeneous initial nucleation. Small-scale ice crystal complexity was deduced from measurements of spatially resolved single particle light scattering patterns by the latest version of the Small Ice Detector (SID-3). It was found that a high crystal complexity dominates the microphysics of the simulated clouds and the degree of this complexity is dependent on the available water vapor during the crystal growth. Indications were found that the small-scale crystal complexity is influenced by unfrozen H 2 SO 4 /H 2 O residuals in the case of homogeneous initial ice nucleation. Angular light scattering functions of the simulated ice clouds were measured by the two currently available airborne polar nephelometers: the polar nephelometer (PN) probe of Laboratoire de Métérologie et Physique (LaMP) and the Particle Habit Imaging and Polar Scattering (PHIPS-HALO) probe of KIT. The measured scattering functions are featureless and flat in the side and backward scattering directions. It was found that these functions have a rather low sensitivity to the small-scale crystal complexity for ice clouds that were grown under typical atmospheric conditions. These results have implications for the microphysical properties of cirrus clouds and for the radiative transfer through these clouds.

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“…Xie [24] concluded that statistical and filter based approaches are those most widely applied. Attempts have been made to find correlations between features in images and the surface properties of manufactured components, such as roughness [27], [28], [29]. Others have analyzed tool wear [30] and the rugosity of metal parts [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Making Tactile Textures with Predefined Affective Properties

Elkharraz

Thumfart

Akay

et al. 2014

IEEE Trans. Affective Comput.

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Abstract-A process for the design and manufacture of 3D tactile textures with predefined affective properties was developed. Twenty four tactile textures were manufactured. Texture measures from the domain of machine vision were used to characterize the digital representations of the tactile textures. To obtain affective ratings, the textures were touched, unseen, by 107 participants who scored them against natural, warm, elegant, rough, simple, and like, on a semantic differential scale. The texture measures were correlated with the participants' affective ratings using a novel feature subset evaluation method and a partial least squares genetic algorithm. Six measures were identified that are significantly correlated with human responses and are unlikely to have occurred by chance. Regression equations were used to select forty eight new tactile textures that had been synthesized using mixing algorithms and which were likely to score highly against the six adjectives when touched by participants. The new textures were manufactured and rated by participants. It was found that the regression equations gave excellent predictive ability. The principal contribution of the work is the demonstration of a process, using machine vision methods and rapid prototyping, which can be used to make new tactile textures with predefined affective properties.

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Grinding surface roughness measurement based on the co-occurrence matrix of speckle pattern texture

Cited by 75 publications

References 20 publications

Incidence of rough and irregular atmospheric ice particles from Small Ice Detector 3 measurements

Incidence of rough and irregular atmospheric ice particles from Small Ice Detector 3 measurements

Cloud chamber experiments on the origin of ice crystal complexity in cirrus clouds

Making Tactile Textures with Predefined Affective Properties

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