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Machine Vision Applications

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:

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Machine Vision Applications

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  1. Machine Vision Applications Case Study No. 1 Analysing Surface Texture

  2. 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.

  3. 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

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

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

  6. 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)

  7. 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

  8. 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)

  9. Making Decisions • Pattern Recognition • Classification • Linear • Compound Classifier • Learning • Two-class learning • Single-class learning

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