{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/41452"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/41452","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Synthesis of Progressive Varying Texture Through Blending Laplacian Pyramid Coefficients","abstract":"We generate a texture synthesis algorithm where there is gradual progression of merged textures in the output image. The goal of the algorithm is to merge the textures based on given measure of texture proportion. The proportion of texture contents defines how much of textures from each input will be preserved in the output and how the distribution of texture will occur after merging. The algorithm is applicable for any random phase texture as well as some non-random phase textures which are stochastic and there is no well-defined visual structures. Our method of progressively variant texture synthesis is distinguished from previous techniques by being able to produce a gradual as well as scattered distribution of merged textures in the output. This irregular distribution gives a realistic view in the merged region of output.","abstract_html":"We generate a texture synthesis algorithm where there is gradual progression of merged textures in the output image. The goal of the algorithm is to merge the textures based on given measure of texture proportion. The proportion of texture contents defines how much of textures from each input will be preserved in the output and how the distribution of texture will occur after merging. The algorithm is applicable for any random phase texture as well as some non-random phase textures which are stochastic and there is no well-defined visual structures. Our method of progressively variant texture synthesis is distinguished from previous techniques by being able to produce a gradual as well as scattered distribution of merged textures in the output. This irregular distribution gives a realistic view in the merged region of output.","abstract_has_math":false,"creators":["Moitry, Das"],"institution":"Carleton University","degree_name":"Master of Computer Science (M.C.S.)","degree_level":"Master's","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-24T01:34:20Z","subjects":[],"languages":["en"],"rights":["Copyright © 2020 the author(s). 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