Curvature and shape variance based landmark tagging methods for building statistical object models

2009 ◽  
Author(s):  
Sylvia Rueda ◽  
Jayaram K. Udupa ◽  
Li Bai
2002 ◽  
Author(s):  
Matthew A. Kupinski ◽  
Eric Clakrson ◽  
Harrison H. Barrett

Author(s):  
Zoltan-Csaba Marton ◽  
Lucian Goron ◽  
Radu Bogdan Rusu ◽  
Michel Beetz
Keyword(s):  

Cytometry ◽  
2003 ◽  
Vol 56A (1) ◽  
pp. 23-36 ◽  
Author(s):  
Gang Lin ◽  
Umesh Adiga ◽  
Kathy Olson ◽  
John F. Guzowski ◽  
Carol A. Barnes ◽  
...  

2021 ◽  
Author(s):  
Raffaella Brumana ◽  
Chiara Stanga ◽  
Fabrizio Banfi

AbstractThe paper focuses on new opportunities of knowledge sharing, and comparison, thanks to the circulation and re-use of heritage HBIM models by means of Object Libraries within a Common Data Environment (CDE) and remotely-accessible Geospatial Virtual Hubs (GVH). HBIM requires a transparent controlled quality process in the model generation and its management to avoid misuses of such models once available in the cloud, freeing themselves from object libraries oriented to new buildings. The model concept in the BIM construction process is intended to be progressively enriched with details defined by the Level of Geometry (LOG) while crossing the different phases of development (LOD), from the pre-design to the scheduled maintenance during the long life cycle of buildings and management (LLCM). In this context, the digitization process—from the data acquisition until the informative models (scan-to-HBIM method)—requires adapting the definition of LOGs to the different phases characterizing the heritage preservation and management, reversing the new construction logic based on simple-to-complex informative models. Accordingly, a deeper understanding of the geometry and state of the art (as-found) should take into account the complexity and uniqueness of the elements composing the architectural heritage since the starting phases of the analysis, adopting coherent object modeling that can be simplified for different purposes as in the construction site and management over time. For those reasons, the study intends (i) to apply the well-known concept of scale to the object model generation, defining different Grades of Accuracy (GOA) related to the scales (ii) to start fixing sustainable roles to guarantee a free choice by the operators in the generation of object models, and (iii) to validate the model generative process with a transparent communication of indicators to describe the richness in terms of precision and accuracy of the geometric content here declined for masonry walls and vaults, and (iv) to identifies requirements for reliable Object Libraries.


2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
Author(s):  
Christian Eckes ◽  
Jochen Triesch ◽  
Christoph von der Malsburg

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.


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