R
Ralph R. Martin
Researcher at Cardiff University
Publications - 286
Citations - 12807
Ralph R. Martin is an academic researcher from Cardiff University. The author has contributed to research in topics: Image segmentation & Boundary representation. The author has an hindex of 51, co-authored 282 publications receiving 10961 citations. Previous affiliations of Ralph R. Martin include University of Wales & Hungarian Academy of Sciences.
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Reverse engineering of geometric models—an introduction
TL;DR: Specific issues addressed include characterization of geometric models and related surface representations, segmentation and surface fitting for simple and free-form shapes, multiple view combination and creating consistent and accurate B-rep models.
Journal Article
An overview of genetic algorithms: Part 1, fundamentals
TL;DR: Genetic Algorithms (GAs) are adaptive methods which may be used to solve search and optimisation problems based on the genetic processes of biological organisms, which simulate those processes in natural populations which are essential to evolution.
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Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
Gary K. L. Tam,Zhi-Quan Cheng,Yu-Kun Lai,Frank C. Langbein,Yonghuai Liu,David Marshall,Ralph R. Martin,Xianfang Sun,Paul L. Rosin +8 more
TL;DR: This study serves to give a comprehensive survey of both types of registration, focusing on three-dimensional point clouds and meshes, and shows how overfitting arises in nonrigid registration and the reasons for increasing interest in intrinsic techniques.
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PCT: Point cloud transformer
TL;DR: A novel framework based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing, is presented, which is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
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A sequential niche technique for multimodal function optimization
TL;DR: An algorithm based on a traditional genetic algorithm that involves iterating the GA but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found.