Machine Vision Applications Case Study No. 1 Analysing Surface Texture

Texture Often impossible to define mathematically Good and bad texture is recognisable, by its physical effect, geometry, physics (e.g. conductivity, taking stains), chemistry or visual appearance. For example: –Apple gives a pleasant biting sensation –Paint peals quickly –Measurable surface roughness We have to learn what a good texture is.

Sample images provided with QT Creased fabric, 28 Coffee beans, 4 Metal sieve, 2 Cork, 5 Cookie, 109 Machined metal surface, 103 Woven cane-work, 156 Printed text, 95

Potential Applications Cork Paint Steel (microscopic scale) Fruit (microscopic scale) Wood Paper Coated surfaces Abbrasive sheet (sand-paper) Fabrics

Example Cork - background was modified by software Local area histogram equalisation

Lighting & Viewing Grazing illumination (Method 13) Polarising (Methods 81 & 84) Omni-directional (Method 10) Diffuse front (Method 11) Coaxial illumination & viewing (Method 31) 45˚ illumination (Method 56)

Preprocessing High-pass filtering (caf(?), sub) Grey-scale morphology (dil and ero) Local-area histogram equalisation (lhq) –Conceptual basis –Implementation avoiding histogram equalisation –Coping with large processing windows Threshold to generate a binary imagedecisions about texture Learning –Learning

Texture Measurements (IVSI, §2.7) Cross-correlation Autocorrelation Spatial dependency matrix –Grey-level –Binary Counting zero-crossings Frequency / sequency / wavelet analysis Morphology (opening / closing with sundry SEs)

Making Decisions Pattern Recognition Classification –Linear –Compound Classifier Learning –Two-class learning –Single-class learning