Shape Retrieval Methods for Architectural 3D Models

This thesis introduces new methods for content-based retrieval of architecture-related 3D models. We thereby consider two different overall types of architectural 3D models. The first type consists of context objects that are used for detailed design and decoration of 3D building model drafts. This includes e.g. furnishing for interior design or barriers and fences for forming the exterior environment. The second type consists of actual building models. To enable efficient content-based retrieval for both model types that is tailored to the user requirements of the architectural domain, type-specific algorithms must be developed. On the one hand, context objects like furnishing that provide similar functions (e.g. seating furniture) often share a similar shape. Nevertheless they might be considered to belong to different object classes from an architectural point of view (e.g. armchair, elbow chair, swivel chair). The differentiation is due to small geometric details and is sometimes only obvious to an expert from the domain. Building models on the other hand are often distinguished according to the underlying floor- and room plans. Topological floor plan properties for example serve as a starting point for telling apart residential and commercial buildings. The first contribution of this thesis is a new meta descriptor for 3D retrieval that combines different types of local shape descriptors using a supervised learning approach. The approach enables the differentiation of object classes according to small geometric details and at the same time integrates expert knowledge from the field of architecture. We evaluate our approach using a database containing arbitrary 3D models as well as on one that only consists of models from the architectural domain. We then further extend our approach by adding a sophisticated shape descriptor localization strategy. Additionally, we exploit knowledge about the spatial relationship of object components to further enhance the retrieval performance. In the second part of the thesis we introduce attributed room connectivity graphs (RCGs) as a means to characterize a 3D building model according to the structure of its underlying floor plans. We first describe how RCGs are inferred from a given building model and discuss how substructures of this graph can be queried efficiently. We then introduce a new descriptor denoted as Bag-of-Attributed-Subgraphs that transforms attributed graphs into a vector-based representation using subgraph embeddings. We finally evaluate the retrieval performance of this new method on a database consisting of building models with different floor plan types. All methods presented in this thesis are aimed at an as automated as possible workflow for indexing and retrieval such that only minimum human interaction is required. Accordingly, only polygon soups are required as inputs which do not need to be manually repaired or structured. Human effort is only needed for offline groundtruth generation to enable supervised learning and for providing information about the orientation of building models and the unit of measurement used for modeling

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Feature Extraction Methods for Character Recognition

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Protein Shape Description and its Application to Shape Comparison

ProSHADE Repository: http://fg.oisin.rc-harwell.ac.uk/projects/proshade/There are currently over 138, 000 known macromolecular structures deposited in the wwPDB (Worldwide Protein Data Bank) database. While all the macromolecular structure files contain information about a particular structure, the collection of these files also allows combining the macromolecular structures to obtain statistical information about macromolecules in general. This fact has been the basis for many structural biology methods including the molecular replacement method used in X-ray crystallography or homologous structure restraints in the refinement methods. With the success of methods based on prior information, it is feasible that novel methods could be developed and current methods improved using further prior information; more specifically, by using the structure density-map shape similarity instead of sequence or model similarity. Therefore, this project introduces a mathematical framework for computing three different measures of macromolecular three-dimensional shape similarity and demonstrates how these descriptors can be applied in symmetry detection and protein-domain clustering. The ability to detect cyclic (C), dihedral (D), tetrahedral (T), octahedral (O) and icosahedral (I) symmetry groups as well as computing all associated symmetry elements has direct applications in map averaging and reducing the storage requirements by storing only the asymmetric information. Moreover, by having the capacity to find structures with similar shape, it was possible to reduce the size of the BALBES protein domain database by more than 18.7% and thus achieve proportional speed-up in the searching parts of its applications. Finally, the development of the method described in this project has many possible applications throughout structural biology. The method could, for example, facilitate matching and fitting of protein domains into the density maps produced by the electron-microscopy techniques, or it could allow for molecular-replacement candidate search using shape instead of sequence similarity. To allow for the development of any further applications, software for applying the methods described here is also presented and released for the community.Medical Research Council ( MC_US_A025_1012

The 1995 Science Information Management and Data Compression Workshop

This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center