Tai, W.-P.W.-P.TaiCHENG-YUAN LIOU2018-09-102018-09-10200001782789http://www.scopus.com/inward/record.url?eid=2-s2.0-0033703835&partnerID=MN8TOARShttp://scholars.lib.ntu.edu.tw/handle/123456789/289464https://www.scopus.com/inward/record.uri?eid=2-s2.0-0033703835&doi=10.1007%2fs003710050199&partnerID=40&md5=a5b711fce512a2acb32a5a990e71b10aConformal mappings are incorporated into the self-organization model to represent images harmonically. This network is used to partition an image into quadrilateral regions, where each region contains similar features. We then map each region to a corresponding square region to unify information representation and facilitate computations. This mapping is constructed to preserve spatial information while complying with the conformal property of the network. An approximated image in each square region provides us with an effective representation of the image in both modeling and compression applications. This approach has been particularly developed for large continues images.application/pdf485148 bytesapplication/pdf[SDGs]SDG10Algorithms; Approximation theory; Computer simulation; Conformal mapping; Harmonic analysis; Image compression; Image reconstruction; Integral equations; Mathematical models; Neural networks; Partial differential equations; Vector quantization; Image modeling; Image partitioning; Image representation; Self organizing network; Image segmentationjournal article10.1007/s0037100501992-s2.0-0033703835