Defect Detection on Inclined Textured Planes Using the Shape from Texture Method and the Delaunay Triangulation

We present one method for detecting defects on an inclined textured plane. This method uses a combination of a shape from texture (SFT) method with the Delaunay triangulation technique. The SFT method provides the theoretical equation of the plane orientation in two steps. First, a wavelet decomposition allows us to build an image of the inverse of the local frequency, that is the scale, that we call the local scales map. Then we perform an interpolation of this map using the equation of the theoretical variation of the scales. With the interpolation parameters it is possible to extract the texels by the use of an adaptive thresholding for each pixel of this map. Then we compute the centers of each texel in order to match a mesh on it after processing a Delaunay triangulation. When there is a defect, the regularity of the triangulation is disturbed, so one hole appears in the mesh.

[1]  David C. Knill,et al.  Surface orientation from texture: ideal observers, generic observers and the information content of texture cues , 1998, Vision Research.

[2]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[4]  Michael Unser,et al.  Multiresolution Feature Extraction and Selection for Texture Segmentation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Wen-Liang Hwang,et al.  Shape from texture based on the ridge of continuous wavelet transform , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[6]  Jonas Gårding,et al.  Direct Estimation of Shape from Texture , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Sylvie Lelandais,et al.  A shape from texture method based on local scales extraction: precision and results , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  John Krumm,et al.  Local spatial frequency analysis of image texture , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[10]  Laurent Boutte.  Extraction des echelles locales appliquee au calcul de l'orientation d'un plan texture , 2000 .

[11]  Alan C. Bovik,et al.  Shape from Texture Using Local Spectral Moments , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Toshiyuki Sakai,et al.  Obtaining Surface Orientation from Texels under Perspective Projection , 1981, IJCAI.

[13]  Kenichi Kanatani,et al.  Shape from Texture: General Principle , 1989, Artif. Intell..

[14]  J. Aloimonos.  Shape from texture , 1988, Biological cybernetics.

[15]  Fumiaki Tomita,et al.  Computer analysis of visual textures , 1990 .

[16]  L. Finkel,et al.  Characterization of the spatial-frequency spectrum in the perception of shape from texture. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Tony Lindeberg,et al.  Shape from texture from a multi-scale perspective , 1993, 1993 (4th) International Conference on Computer Vision.

[18]  Sylvie Lelandais,et al.  Texel extraction on inclined textures by adaptive thresholding of local scales , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.