{"id":121,"date":"2020-02-27T09:26:09","date_gmt":"2020-02-27T09:26:09","guid":{"rendered":"https:\/\/arms10.org\/publication\/?p=121"},"modified":"2020-03-03T06:18:58","modified_gmt":"2020-03-03T06:18:58","slug":"thread-position-cnc-sewing","status":"publish","type":"post","link":"https:\/\/arms10.org\/publication\/thread-position-cnc-sewing\/","title":{"rendered":"Thread Position CNC Sewing"},"content":{"rendered":"\n<h2> <a href=\"https:\/\/www.vssewingmachine.in\/\">Thread Position CNC Sewing<\/a> <\/h2>\n\n\n\n<p> Due  to  the  elasticity  of  thread  and  tissue  material,the  stitches  made  by  a  computer  program  on  a  CNC  sewingmachine  are  shifted  from  their  desired  positions.  The  mismatchleads  to  a  visually  inferior  appearance  of  the  resulting  threadpattern  compared  to  the  one  intended  by  the  manufacturer.This  paper  introduces  a  camera  based  inspection  system  thathelps to find and evaluate the distortion vector for each individualstitch. These vectors can facilitate the automatic reprogrammingof  corrected  stitch  positions  in  the  CNC  sewing  program  tominimize  the  final  distortion.  An  image  processing  pipeline  isproposed,  consisting  of  thread  detection  and  model-based  regis-tration, finding an assignment of thread pixels and model stitchpositions. The corresponding distortion vectors are computed forevery  individual  stitch. <a href=\"https:\/\/www.vssewingmachine.in\/\">VS Sewing Machines<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.vssewingmachine.in\/\"><img loading=\"lazy\" width=\"620\" height=\"726\" src=\"https:\/\/arms10.org\/publication\/wp-content\/uploads\/2020\/02\/Correct2ndImage-c32e6.jpg\" alt=\"Thread Position CNC Sewing\" class=\"wp-image-122\" srcset=\"https:\/\/arms10.org\/publication\/wp-content\/uploads\/2020\/02\/Correct2ndImage-c32e6.jpg 620w, https:\/\/arms10.org\/publication\/wp-content\/uploads\/2020\/02\/Correct2ndImage-c32e6-256x300.jpg 256w\" sizes=\"(max-width: 620px) 100vw, 620px\" \/><\/a><\/figure>\n\n\n\n<p> Due  to  the  elasticity  of  thread  and  tissue  material,the  stitches  made  by  a  computer  program  on  a  CNC  sewingmachine  are  shifted  from  their  desired  positions.  The  mismatchleads  to  a  visually  inferior  appearance  of  the  resulting  threadpattern  compared  to  the  one  intended  by  the  manufacturer.This  paper  introduces  a  camera  based  inspection  system  thathelps to find and evaluate the distortion vector for each individualstitch. These vectors can facilitate the automatic reprogrammingof  corrected  stitch  positions  in  the  CNC  sewing  program  tominimize  the  final  distortion.  An  image  processing  pipeline  isproposed,  consisting  of  thread  detection  and  model-based  regis-tration, finding an assignment of thread pixels and model stitchpositions. The corresponding distortion vectors are computed forevery  individual  stitch.An  image  processing  pipeline  was  designed  for  the  threaddetection. The complete workflow is visualized in Figure 3.At all times, knowledge about the desired thread pattern modelis available.A.  Two Partial Luminance ImagesThe composed camera RGB-image is converted to a single-channel luminance imageL(x,y) = 0.3\u2217R(x,y)+0.59\u2217G(x,y)+0.11\u2217B(x,y).(1)The  conversion  is  based  on  the  assumption,  that  the  threadappears  either  brighter  or  darker  than  the  tissue  background.However, it is unknown in advance which appearance is given.Therefore,  two  partial  images  are  generated.  The  positiveimageI+(x,y)only  contains  pixels  brighter  than  the  tissuemean,  whereas  the  negative  imageI\u2212(x,y)only  containspixels darker than the mean,I+(x,y) = max(0, L(x,y)\u2212m)(2)I\u2212(x,y) = max(0, m\u2212L(x,y))(3)m= mean(L(x,y)).(4)By  separation  into  partial  images,  the  pixels  representingthe  thread  will  only  be  visible  in  one  of  them.  They  willalways appear as a bright structure. Next to the actual threadpixels, there will also appear spurious pixels from noisy tissuestructures.  They  resemble  thread-like  structure  parts  and  arestochastically distributed.B.  Frangi FilteringA Frangi filter [3] is applied on both partial images. The fil-ter operation basically consists of a pixel-wise computation ofthe Hessian-Matrix and a Gaussian-shaped smoothing kernel.It is also known as a vesselness filter and was originally intro-duced in the context of medical image analysis to emphasizepixels  that  are  embedded  in  vessel-like  structures.  However,an  elongated  and  thin  appearance  is  not  only  characteristicfor vessels inside the human body but also for the consideredthread  within  this  work.  It  is  therefore  natural  to  adopt  theestablished methodology for the given task. The result are twoimages,IF r+andIF r\u2212, with each pixel value containing theprobability that it is embedded in a thread-like structure.C.  Selection of the Thread ImageBased on the supplied model prior, an approximate numberof  expected  thread  pixels  can  be  estimated.  This  expectationcan be turned into a thresholding operation, that is performedon  both  images.  Since  one  of  the  filtered  images  containsboth the thread as well as background noise, while the otherimage  only  contains  background  noise,  a  robust  detection  ofthe  thread  image  is  straight  forward.  The  result  is  a  singlebinary image,wF r(x,y), with a pixel value of 1 indicating athread pixel.D.  Classification of Pure Thread PixelsThe  previous  step  provides  a  mask  for  pixels  that  areembedded  in  a  thread-like,  i.e.  elongated  and  thin,  structure.Yet,   pixels   lying   not   directly   on   the   thread   but   nearbymay   be   included.   Therefore,   the   mask   is   refined   usingan   expectation   maximization   (EM)   algorithm   [1].   Therefinement  is  no  longer  performed  on  the  luminance  image,but  on  the  RGB-image.  As  initialization,  the  tissue  RGBvalues   from   the   background   removal   step   are   taken   forthe  tissue  mean  and  covariance  values.  The  thread  meanand  covariance  values  are  derived  from  all  pixels  maskedbywF r.   The   iterative   EM   algorithm   results   in   a   singlebinary  image,wges(x,y),  with  a  pixel  value.<a href=\"https:\/\/www.vssewingmachine.in\/\">https:\/\/www.vssewingmachine.in\/<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/g.page\/vssewing?share\">https:\/\/g.page\/vssewing?share<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Thread Position CNC Sewing Due to the elasticity of thread and tissue material,the stitches made by a computer program on a CNC sewingmachine are shifted from their desired positions. The mismatchleads to a visually inferior appearance of the resulting threadpattern&#8230; <a class=\"direadmore\" href=\"https:\/\/arms10.org\/publication\/thread-position-cnc-sewing\/\"> Continue Reading&#8230;<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[89],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v15.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Thread Position CNC Sewing - International Conference Publications<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/arms10.org\/publication\/thread-position-cnc-sewing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Thread Position CNC Sewing - International Conference Publications\" \/>\n<meta property=\"og:description\" content=\"Thread Position CNC Sewing Due to the elasticity of thread and tissue material,the stitches made by a computer program on a CNC sewingmachine are shifted from their desired positions. 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