01964nam a2200277 a 4500952012200000999001700122001001400139003000700153005001700160008004100177020002200218039008600240040001300326090002400339100001600363245011900379260003700498300003500535500005300570520091900623650004801542650002801590700002101618700003001639942001701669  00104071a10000b10000d2019-09-04l0oTS1449 .Z34 2006 rsp0000018219r2019-09-04 00:00:00t1w2019-09-04ySPECIAL  c11398d11404vtls000018263KUKTEM20251117141112.0070215t2004    my     g t    001 0 may d  aSC00000102(Local) 9a201905291436bnadiac201107131902dVLOADc201105231428dFidac200908141302dVLOAD  aUMPcUMP  aTS1449 .Z34 2006 rs0 aZalili Musa10aSistem pengesanan kerosakan pada jalinan tekstil /cZalili binti Musa, Rohani Abu Bakar, Tuty Asmawaty Abdul Kadir  aKuantan, Pahang :bKUKTEM,c2006  axvii, 211 p. :bill. ;c30 cm.  aResearch final report - vot RDU 05/1/17 (KUKTEM)  aThe purpose of this study was to determine the best methodology, techniques, and algorithms in detecting defects on textile webs through several tests on the prototypes developed in the laboratory. Existing methodologies are based on statistical methods that emphasize on pixel density and relationship of grey level among neighboring pixels. This method, however, is not very effective in detecting defects on textile webs. From a series of tests conducted in this study, a methodology that was enhanced with a pre-processor module (Lili Methodology) was able to detect defects on all the five types of textile webs at 100% level. Four main modules used in this methodology were image acquisition, pre-processor, extraction, and texture analysis (see Figure 1). For each module, several enhanced techniques and algorithms that were based on existing techniques and algorithms were used to produce optimum results. 0aTextile fibersxAnalysisxComputer programs 0aTextile fibersxTesting0 aRohani Abu Bakar0 aTuty Asmawaty Abdul Kadir  2lcccSPECIAL