Pixel Evaluation
Digital processing counts individual dots inside a captured flax sliver photograph to separate dark trash particles from pale bast fibres. Image analysis thresholding sets an arbitrary brightness boundary where every sensor signal above the mark becomes counted as clean material and every signal below the mark turns black for rejection. Scanners inside Chinese spinning mills apply this numerical filter during high speed web inspection before the draw frame stage.
Camera sensors record reflected light intensity across a grey scale ranging from zero to two hundred fifty five. Software algorithms evaluate that matrix to calculate particle distribution percentages recorded daily in the mill quality ledger.
Defect Separation
Software routines isolate foreign matter by dropping all pixels falling outside an established brightness range. Dark shive fragments and residual root ends absorb illumination differently than cellulose strands. Image analysis thresholding draws a strict mathematical line across the histogram distribution curve.
Operators adjust the division point manually whenever raw material batches shift from dew retted flax to water retted flax. Mill acceptance protocols enforce strict limits on total contamination mass allowed per kilogram of sliver. Buyer specifications demand lower trash counts than internal mill standards to prevent drafting roller jams during subsequent drafting and roving steps.
Area Quantification
Machine vision instruments compute total contaminated surface coverage by summing rejected pixels across the scan width. Image analysis thresholding converts continuous optical gradations into binary raster data for automated sorting logic. Calculated ratios dictate whether an entire production lot passes outgoing inspection or returns for mechanical re-combing.
Final export certificates record these exact contamination percentages alongside tensile strength metrics.
