1 / 12

Fast Exact Euclidean Distance (FEED) Transformation

Fast Exact Euclidean Distance (FEED) Transformation. Theo Schouten Egon van den Broek Radboud University Nijmegen. Distance transformation. distance map D(p) = min { dist(p,q), q  O } approximation of Euclidean Rosenfeld & Pfaltz local, parallel or sequential Borgefors

moswen
Download Presentation

Fast Exact Euclidean Distance (FEED) Transformation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Fast Exact Euclidean Distance (FEED)Transformation Theo Schouten Egon van den Broek Radboud University Nijmegen FEED

  2. Distance transformation • distance map D(p) = min { dist(p,q), q  O } • approximation of Euclidean • Rosenfeld & Pfaltz • local, parallel or sequential • Borgefors • chamfer, weighted distances FEED

  3. Euclidean distance • not by local operations • disconnected Voronoi tile • often right, sometimes wrong ED • correction Cuisenaire & Macq CVIU 76 (1999) FEED

  4. Principle of FEED • D(p) = if (p  O) then 0 else  for each q  O for each p  O D(p) = min ( D(p), ED(q,p)) • inverse of definition • correct, terrible slow FEED

  5. Speed up, step 1 • reduce q  O to consider • only the border pixels of O x Border: q  O x x x at least 1 4-conn p  O x FEED

  6. Speed up, step 2 • pre-computation of ED(q,p) • matrix, size of image translation, reflection invariant • M = fnon-decr( ED), like square • size can be reduced • in case max. dist. is known • only up to a maximum is interesting FEED

  7. Speed up, step 3 • reduce p  O to update per B FEED

  8. Balance • time lost: • searching object pixels • administration bisection line • against time gained: • not updating certain p  O • optimum, distribution object pixels FEED

  9. Results • Shih & Liu 4-scan ED (PR 31, 1998) • not their correction method • test images, object-like images • FEED is faster, up to 2.7 • up to 4.5 reduced M • random dot images, faster < 15% • FEED uses less memory FEED

  10. Applications • human color categories • black, white, gray, red, green, blue, yellow, brown, purple, pink, orange • 216 web-safe colors • classify 2563 colors • RGB->HSI, SI: 3 /8, 3, HI: 8 • content based image retieval, texture a FEED

  11. Further developments • step 3: faster, simpler • formal proofs • partial maps, fixed objects + moving objects in video • color space applications FEED

  12. FEED conclusions • EDT inverse definition • simple, correct, slow • 3 speed up approaches • faster than 4-scan method • up to maximum, partial maps • human-centered color space FEED

More Related