A framework for Internet-based surface texture analysis and information system

2005 ◽  
Vol 29 (3) ◽  
pp. 298-306 ◽  
Author(s):  
Son H. Bui ◽  
B. Muralikrishnan ◽  
J. Raja
Keyword(s):  
Information System ◽  
Texture Analysis ◽  
Surface Texture ◽  
Surface Texture Analysis

AN AUTOREGRESSIVE APPROACH TO SURFACE TEXTURE ANALYSIS

1994 ◽  
Vol 08 (04) ◽  
pp. 845-857 ◽  
Author(s):  
DINESH P. MITAL ◽  
GOH WEE LENG
Keyword(s):  
Aspect Ratio ◽  
Random Field ◽  
Texture Analysis ◽  
Surface Texture ◽  
Field Model ◽  
Invariant Property ◽  
Circular Area ◽  
Pixel Image ◽  
Rotational Invariant ◽  
Surface Texture Analysis

The use of autoregressive models in textual analysis holds great potential. Coupling the technique to a circular neighbourhood set imparts a rotational invariant property to it. This was demonstrated by Kashyap and Khotanzad in their model called the Circular Symmetric Autogressive (CSAR) Random Field model. The short-coming in this very ingenious proposal is that it is set in a background of square pixels and the rotational invariant property of the model fails in cases when the aspect ratio of the pixels are not at unity. This paper proposes a major modification to the CSAR to render the model rotational invariant under all configurations of pixel implementation. It is based on the area segments covered by a circle set in a 3×3 neighbourhood. We call it the Circular Area Autoregressive (CAAR) model. The results obtained from the CAAR showed much better consistency over that of the CSAR when a non-square pixel image was used.

A Modified Fourier Method of Shape and Surface Texture Analysis of Planar Sections of Particles

1977 ◽  
Vol 5 (4) ◽  
pp. 292 ◽  
Author(s):  
RL Meltzer ◽  
YR Fiorini ◽  
RT Horstman ◽  
IC Moore ◽  
AL Batik ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Surface Texture ◽  
Fourier Method ◽  
Surface Texture Analysis

Engineering Surface Analysis With Different Wavelet Bases

2003 ◽  
Vol 125 (4) ◽  
pp. 844-852 ◽  
Author(s):  
Shengyu Fu ◽  
B. Muralikrishnan ◽  
J. Raja
Keyword(s):  
Texture Analysis ◽  
Surface Analysis ◽  
Surface Texture ◽  
Filter Banks ◽  
Performance Standards ◽  
Superior Performance ◽  
Close Monitoring ◽  
Wavelet Bases ◽  
Surface Profiles ◽  
Surface Texture Analysis

Traditional surface texture analysis involves filtering surface profiles into different wavelength bands commonly referred to as roughness, waviness and form. The primary motivation in filtering surface profiles is to map each band to the manufacturing process that generated the part and the intended functional performance of the component. Current trends in manufacturing are towards tighter tolerances and higher performance standards that require close monitoring of the process. Thus, there is a need for finer bandwidths for process mapping and functional correlation. Wavelets are becoming increasingly popular tools for filtering profiles in an efficient manner into multiple bands. While they have broadly been demonstrated as having superior performance and capabilities than traditional filtering, fundamental issues such as choice of wavelet bases have remained unaddressed. The major contribution of this paper is to present the differences between wavelets in terms of the transmission characteristics of the associated filter banks, which is essential for surface analysis. This paper also reviews fundamental mathematics of wavelet theory necessary for applying wavelets to surface texture analysis. Wavelets from two basic categories—orthogonal wavelet bases and biorthogonal wavelet bases are studied. The filter banks corresponding to the wavelets are compared and multiresolution analysis on surface profiles is performed to highlight the applicability of this technique.

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