Visual Measurement Of Rough Surface Based On Light Scattering Theory

Surface roughness plays an important role in the fields of mechanical manufacturing,robot perception,natural interaction and human-computer cooperation.The measurement of surface roughness has attracted extensive attention from researchers and engineers.With the development of computer vision and machine vision,researchers have made many outstanding contributions to theory and visual measurement technologies of surface roughness.The core issues associated with this area are: how to construct intrinsic relationship between image features and roughness parameters,and how to design and extract image features of surface roughness.To address these two issues,the mathematical model of rough surfaces and its measurement indicators in spatial domain are derived based on surface scattering theory.Then,the irradiance distribution functions of the scattered light on different scale rough surfaces are derived using the theory of geometric optics and physical optics respectively.The intrinsic mapping between the rough surface and its associated image is established based on the linear camera model.Based on the above theories,image features in multi-scale rough surfaces are extracted,and the surface roughness is measured by machine vision.The details are as follows:1)Two mathematical models of the rough surface are established,i.e.the height distribution model,and the slope distribution model.Assuming that the height and the slope of rough surfaces obey the Gaussian distribution respectively,rough surface is modeled as a random process whose height follows a Gaussian distribution,and this random process is simulated based on the two-dimensional linear filter and Fourier transform.On the mathematical relationship between height and slope,the Gaussian slope distribution model of rough surface is deduced.The theoretical analysis and results demonstrate that the single-parameter slope distribution model is suitable for the situation of large-scale roughness and low accuracy,while multi-parameter height distribution model shows an advantage in the case of small-scale roughness and high precision.2)Irradiance distribution and simulation on scattering of rough surfaces in image domain.Based on the rough surface model with Gaussian slope distribution and with Gaussian height distribution,the reflection on rough surfaces is analyzed from the perspective of geometric optics and physical optics,and scattering models of rough surfaces in radiance domain is proposed.In order to construct the intrinsic relationship between image features and roughness parameters of rough surfaces,the radiance in the radiation domain is mapped to the image domain according to the linear camera model,and the numerical simulations are carried out in the image domain.The simulation results show that the gray level from a rough surface with a Gaussian slope distribution is still Gaussian distribution.For a rough surface with Gaussian height distribution,the point blur function in the reflective image can be approximated by a Gaussian function.As the roughness increases,the peak irradiance in image domain decreases gradually.In addition,as the incident angle increases,the peak irradiance in image domain increases gradually.Even for a fixed light source,the irradiance in the image domain is different in different viewing directions.3)Visual perception of large-scale rough surfaces in the maximum variance direction of image patches.Based on the irradiance distribution model of large-scale rough surfaces,the slope of the micro-surface in spatial domain is mapped to the gray level in image domain.In stead of pixel-wised operations,which leads to unstable roughness measurements,a novel visual roughness perception in the maximum variance direction of image patches is proposed in this thesis.Using multi-pixel image patches as samples reduces the instability of single pixel.The patch samples are vectorized to construct a multi-dimensional sample space,and each image patch is mapped to a point in this space.Based on the theory of principal component analysis,it is analyzed that the sampling direction of the maximum variance of all sample points is the direction of the maximum signal-to-noise ratio.Sampling in this direction further improves the signal-to-noise ratio of roughness.Compared with traditional classical algorithms,the proposed algorithm not only improves the robustness of large-scale rough surfaces visual measurement,but also has an obvious advantage in accuracy.4)Roughness measurement of rough surface from line blur functions of reflective images.On the problem that small-scale rough surfaces(especially high-gloss surfaces)are prone to form virtual images or highlights,which seriously affect the image acquisition of surfaces,a new method for visual measurement of rough surfaces based on the line blur function in the reflective images is presented,inspired by the phenomenon that rougher surfaces yield blurrier reflective images.Based on the irradiance distribution model of rough surface scattering in image domain,it is mathematically revealed that the line blur function is the value of the point blur function in the vertical direction of image edges.The experimental results in comparison with the traditional stylus method for measuring small-scale rough surfaces,show that the proposed visual measurement method for surface roughness has good performance in term of accuracy,efficiency and stability.