03089nas a2200601   4500000000100000000000100001000000100002008004100003653003400044653001000078653001200088653001600100653001400116653000700130653001600137653001600153653003300169653003400202653002200236653002200258653001500280653001800295653001900313653003800332653003900370653001100409653002100420653001700441653002100458653001700479653002100496653002200517653002700539653002100566653001300587653002400600653002400624653002200648653002000670653002600690653001900716653001900735653001300754653001700767100001000784700001400794245011000808856014000918300001001058490000701068520139201075022002002467       2022                            d10aIntangible cultural heritages10ahuman10aarticle10aInheritance10aEducation10aAI10acalculation10aClusterings10aconvolutional neural network10aConvolutional neural networks10aCreative products10aCreative products10aCreativity10aDesign method10aEdge detection10aEthical education among educator:10aethical education among educators:10aethics10aEthics education10aFolk customs10ahuman experiment10aImage fusion10aimage generation10aImage generations10aintermethod comparison10amachine learning10apainting10apainting generation10aPainting generation10aPharma industries10aPharma industry10aPhilosophical aspects10aProduct design10aProduct design10areligion10aRNA splicing1 aF. Xu1 aL. Zhiwei00aDesign method of intangible cultural products based on pharma industries ethics education among educators  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136709369&doi=10.5912%2fjcb1035&partnerID=40&md5=6dff81f244ea6abe17ef2ada3f8b2015  a32-410 v273 aA design technique for intangible cultural creative products based on pharma industries ethics education among educator s generation is suggested to address the issue of low image fusion caused by the high cost of clustering calculation in the design of such items. The intangible cultural heritage image is extracted and segmented. The image is divided into different regions according to the gray level of pixels, and the edge detection algorithm of images in different regions of the product is designed to make the region near the picture more fit. The fusion image of intangible cultural heritage features is generated based on pharma industries  ethical education with educators, so as to reduce the cost of clustering calculation and complete the splicing of artistic style. Build the design model of intangible cultural creative products, combine the design creativity, and realize the mutual integration of intangible culture and products. Construct four image sample sets of scenic spots and historic sites, religious beliefs, festival folk customs, and handmade, and test the fusion degree. Taking the festival folk custom image set as an example, the average fusion degree of this method is 0.773, which is 0.133 and 0.145 higher than the comparison method based on convolutional neural network and machine learning, respectively. Therefore, it has a good application effect.  a14628732 (ISSN)