An Improved Error-Diffusion Approach for Generating Mesh Models of Images

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dc.contributor.authorMa, Xiao
dc.contributor.supervisorAdams, Michael D.
dc.date.accessioned2014-11-25T23:16:55Z
dc.date.available2014-11-25T23:16:55Z
dc.date.copyright2014en_US
dc.date.issued2014-11-25
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractTriangle mesh models of images are studied. Through exploration, a computational framework for mesh generation based on data-dependent triangulations (DDTs) and two specific mesh-generation methods derived from this framework are proposed. In earlier work, Yang et al. proposed a highly-effective technique for generating triangle-mesh models of images, known as the error diffusion (ED) method. Unfortunately, the ED method, which chooses triangulation connectivity via a Delaunay triangulation, typically yields triangulations in which many (triangulation) edges crosscut image edges (i.e., discontinuities in the image), leading to increased approximation error. In this thesis, we propose a computational framework for mesh generation that modifies the ED method to use DDTs in conjunction with the Lawson local optimization procedure (LOP) and has several free parameters. Based on experimentation, we recommend two particular choices for these parameters, yielding two specific mesh-generation methods, known as MED1 and MED2, which make different trade offs between approximation quality and computational cost. Through the use of DDTs and the LOP, triangulation connectivity can be chosen optimally so as to minimize approximation error. As part of our work, two novel optimality criteria for the LOP are proposed, both of which are shown to outperform other well known criteria from the literature. Through experimental results, our MED1 and MED2 methods are shown to yield image approximations of substantially higher quality than those obtained with the ED method, at a relatively modest computational cost. For example, in terms of peak-signal-to-noise ratio, our MED1 and MED2 methods outperform the ED method, on average, by 3.26 and 3.81 dB, respectively.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationX. Ma and M. D. Adams. An improved method for generating triangle-mesh models of images. Halifax, NS, Canada, August 2014. Available online at https://projects.cs.dal.ca/cccg2014/proceedings/papers/paper10.pdf.en_US
dc.identifier.bibliographicCitationX. Ma and M. D. Adams. An improved error-diffusion approach for generating mesh models of images. Submitted to Signal Processing on October 29, 2014.en_US
dc.identifier.urihttp://hdl.handle.net/1828/5734
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectImage Representationsen_US
dc.subjectNonuniform Samplingen_US
dc.subjectTriangle Meshesen_US
dc.subjectData-Dependent Triangulationsen_US
dc.subjectError Diffusionen_US
dc.titleAn Improved Error-Diffusion Approach for Generating Mesh Models of Imagesen_US
dc.typeThesisen_US

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