02616nas a2200349   4500000000100000000000100001008004100002260002000043653002000063653002600083653001700109653002500126653003500151653002700186653002300213653002400236653002300260653004800283653002800331653002700359653002600386100001400412700001300426700001500439700001400454245012800468856015300596300001400749490001500763520144600778020004202224       2019                            d  bSpringer Verlag10aVirtual reality10aHistoric preservation10aConservation10aArchitectural design10aBuilding Information Modelling10aHistorical information10aInformation theory10aCultural Experience10aHeritage Landscape10aHeritage Landscape Information Model (HLIM)10aIntegrated informations10aLandscape conservation10aLandscape information1 aChen Yang1 aFeng Han1 aHangbin Wu1 aZhuo Chen00aHeritage Landscape Information Model (HLIM): Towards a Contextualised Framework for Digital Landscape Conservation in China  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85042111381&doi=10.1007%2f978-3-319-73603-7_39&partnerID=40&md5=23992dd790d0c0f878c1acd2e876e505  a1221-12270 v10704 LNCS3 aRandom-needle Embroidery (RNE) is a graceful Chinese Embroidery art enrolled in the World Intangible Heritage. In this paper, we propose a rendering method to translate a reference image into an art image with the style of random-needles. Since RNE artists create artwork by stitching thousands of intersecting threads with complex patterns into an embroidery cloth, the key of RNE rendering is to define its threads distributions in vector space (actual physical space) and generate its artistic styles in pixel space (coordinate system of the image). To this end, we first define “stitch” which is a collection of threads arranged in a certain pattern as the basic rendering primitive. A vector space stitch model is presented, which can automatically generate various thread distributions in stitches. Then, the rendering primitives are generated by rasterizing the stitches on 2D pixel arrays. During runtime, new stitches can be synthesized to portray the image content via sparse modeling based on the pre-defined stitches. In order to avoid mosaic effects, this result is further refined by incorporating local stitch vector constraints, in which we enforce the thread distribution of the local stitch to be similar to its adjacent stitches. Finally, rendering image is generated by placing stitches with different attributes on the canvas. Experiments show that our method can perform fine images with the style of random-needle.  a03029743 (ISSN); 9783319736020 (ISBN)