Shape representation and indexing based on region connection calculus and oriented matroid theory

International Conference on Discrete Geometry for Computer Imagery (DGCI), 2003, Naples (Italy)In this paper a novel method for indexing views of 3D objects is presented. The topological properties of the regions of the views of a set of objects are used to define an index based on the region connection calculus and oriented matroid theory. Both are formalisms for qualitative spatial representation and reasoning and are complementary in the sense that whereas the region connection calculus encodes information about connectivity of pairs of connected regions of the view, oriented matroids encode relative position of the disjoint regions of the view and give local and global topological information about their spatial distribution. This indexing technique is applied to 3D object hypothesis generation from single views to reduce candidates in object recognition processes.Peer Reviewe

Shape representation and indexing based on region connection calculus and oriented matroid theory

International Conference on Discrete Geometry for Computer Imagery (DGCI), 2003, Naples (Italy)In this paper a novel method for indexing views of 3D objects is presented. The topological properties of the regions of the views of a set of objects are used to define an index based on the region connection calculus and oriented matroid theory. Both are formalisms for qualitative spatial representation and reasoning and are complementary in the sense that whereas the region connection calculus encodes information about connectivity of pairs of connected regions of the view, oriented matroids encode relative position of the disjoint regions of the view and give local and global topological information about their spatial distribution. This indexing technique is applied to 3D object hypothesis generation from single views to reduce candidates in object recognition processes.Peer Reviewe

Oriented matroids for shape representation and indexing

Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Port d'Andratx (Spain)In this paper a novel method for indexing views of 3D objects is presented. The topological properties of the regions of the segmented images of the objects are used to define an index based on oriented matroid theory. Oriented matroids, which are projective invariants, encode incidence relations and relative position of the elements of the image and give local and global topological information about their spatial distribution. This indexing technique is applied to 3D object hypothesis generation from single views to reduce the number of candidates in object recognition processes.Peer Reviewe

Oriented matroids for shape representation and indexing

Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Port d'Andratx (Spain)In this paper a novel method for indexing views of 3D objects is presented. The topological properties of the regions of the segmented images of the objects are used to define an index based on oriented matroid theory. Oriented matroids, which are projective invariants, encode incidence relations and relative position of the elements of the image and give local and global topological information about their spatial distribution. This indexing technique is applied to 3D object hypothesis generation from single views to reduce the number of candidates in object recognition processes.Peer Reviewe

QUASII: QUery-Aware Spatial Incremental Index.

With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can be executed efficiently. Such an indexing step requires substantial computing resources and introduces a considerable and growing data-to-insight gap where scientists need to wait before they can perform any analysis. Moreover, scientists often only use a small fraction of the data - the parts containing interesting phenomena - and indexing it fully does not always pay off. In this paper we develop a novel incremental index for the exploration of spatial data. Our approach, QUASII, builds a data-oriented index as a side-effect of query execution. QUASII distributes the cost of indexing across all queries, while building the index structure only for the subset of data queried. It reduces data-to-insight time and curbs the cost of incremental indexing by gradually and partially sorting the data, while producing a data-oriented hierarchical structure at the same time. As our experiments show, QUASII reduces the data-to-insight time by up to a factor of 11.4x, while its performance converges to that of the state-of-the-art static indexes

The OTree: multidimensional indexing with efficient data sampling for HPC

Spatial big data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of petabytes of spatial data per year. However, many authors have pointed out that the lack of specialized frameworks for multidimensional Big Data is limiting possible applications and precluding many scientific breakthroughs. Paramount in achieving High-Performance Data Analytics is to optimize and reduce the I/O operations required to analyze large data sets. To do so, we need to organize and index the data according to its multidimensional attributes. At the same time, to enable fast and interactive exploratory analysis, it is vital to generate approximate representations of large datasets efficiently. In this paper, we propose the Outlook Tree (or OTree), a novel Multidimensional Indexing with efficient data Sampling (MIS) algorithm. The OTree enables exploratory analysis of large multidimensional datasets with arbitrary precision, a vital missing feature in current distributed data management solutions. Our algorithm reduces the indexing overhead and achieves high performance even for write-intensive HPC applications. Indeed, we use the OTree to store the scientific results of a study on the efficiency of drug inhalers. Then we compare the OTree implementation on Apache Cassandra, named Qbeast, with PostgreSQL and plain storage. Lastly, we demonstrate that our proposal delivers better performance and scalability.Peer ReviewedPostprint (author's final draft

Geometry in Medical Imaging: DICOM and NIfTI formats

The spatial relation between the pixels in a medical image and their real-world position is important for clinical image display, surgery planning, image fusion and comparison of images acquired with di erent pixel sizes, orientations, scan- ners or time points. The correct manipulation of this `geometry' or `spatial referencing' can be challenging. This paper aims to provide a clear description of the link between the data in the computer, array indexing and the 3D location where the image data was acquired