A Flexible Content-Adaptive Mesh-Generation
Strategy for Image Representation
Michael D. Adams, Senior Member, IEEE
Based on the greedy-point removal (GPR) scheme of Demaret and Iske, a simple yet highly-effective
framework for constructing triangle-mesh representations of images, called GPRFS, is proposed. By using
this framework and ideas from the error diffusion (ED) scheme (for mesh-generation) of Yang et al.,
a highly effective mesh-generation method, called GPRFS-ED, is derived and presented. Since the ED
scheme plays a crucial role in our work, factors affecting the performance of this scheme are also studied
in detail. Through experimental results, our GPRFS-ED method is shown to be capable of generating
meshes of quality comparable to, and in many cases better than, the state-of-the-art GPR scheme, while
requiring very substantially less computation and memory. Furthermore, with our GPRFS-ED method, one
can easily tradeoff between mesh quality and computational/memory complexity. A reduced complexity
version of the GPRFS-ED method (called GPRFS-MED) is also introduced to further demonstrate the
computational/memory-complexity scalability of our GPRFS-ED method.
Image representations, triangle meshes, mesh generation, greedy point removal, error diffusion.
In the last several years, image representations based on adaptive (i.e., irregular) sampling have