TL;DR: The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature.
Abstract: In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator
TL;DR: In this paper, a Morse-based coding system for representing the hierarchical structure of contours is presented, and the resulting coding works interactively with a range of surface reconstruction systems.
Abstract: Coding system requirements are briefly discussed. Classical Morse theory, which was primarily motivated by the calculus of variations, is reviewed. The limits of the theory are examined, and an extension that enables 3-D surfaces to be accurately reconstructed from cross sections is presented. The resulting coding works interactively with a range of surface reconstruction systems. The prototype coding system is applied to representing the hierarchical structure of contours. >
TL;DR: This work introduces a critical point sensing method that uses range sensors and presents a provably complete algorithm which guarantees that the robot will encounter all the critical points, thereby constructing the full graph, i.e., achieving complete coverage.
Abstract: The goal of coverage path planning is to determine a path that passes a detector over all points in an environment. This work prescribes a provably complete coverage path planner for robots in unknown spaces. We achieve coverage using Morse decompositions which are exact cellular decompositions whose cells are defined in terms of critical points of Morse functions. Generically, two critical points define a cell. We encode the topology of the Morse decomposition using a graph that has nodes corresponding to the critical points and edges representing the cells defined by pairs of critical points. The robot simultaneously covers the space while incrementally constructing this graph. To achieve this, the robot must sense all the critical points. Therefore, we first introduce a critical point sensing method that uses range sensors. Then we present a provably complete algorithm which guarantees that the robot will encounter all the critical points, thereby constructing the full graph, i.e., achieving complete c...
TL;DR: Reeb graphs as discussed by the authors are a fundamental data structure for understanding and representing the topology of shapes and are used in computer graphics, solid modeling, and visualization for applications rangin...
Abstract: Reeb graphs are a fundamental data structure for understanding and representing the topology of shapes. They are used in computer graphics, solid modeling, and visualization for applications rangin...
TL;DR: This article presents a 3D shape matching method for 3D mesh models applied to content-based search in database of 3D objects based on the multiresolution Reeb graph, which reveals very performant for the retrieval and the classification of similar 3D shapes.
Abstract: This article presents a 3D shape matching method for 3D mesh models applied to content-based search in database of 3D objects. The approach is based on the multiresolution Reeb graph (MRG) proposed by Hilaga et al.1 MRG provides a rich representation of shapes able in particular to embed the object topology. In our framework, we consider 3D mesh models of various geometrical complexity, of different resolution, and when available with color texture map. The original approach, mainly based on the 3D object topology, is not accurate enough to obtain satisfying matching. Therefore we propose to reinforce the topological consistency conditions of the matching and to merge within the graph geometrical and visual information to improve matching and calculation of shape similarity between models. Besides, all these new attributes can be freely weighted to fit the user requirements for object retrieval. We obtain a flexible multiresolutional and multicriteria representation that we called augmented multiresolution Reeb graph (aMRG). The approach has been tested and compared with other methods. It reveals very performant for the retrieval and the classification of similar 3D shapes.