An efficient algorithm to compute subsets of points in ℤ n

In this paper we show a more efficient algorithm than that in [8] to compute subsets of points non-congruent by isometries. This algorithm can be used to reconstruct the object from the digital image. Both algorithms are compared, highlighting the improvements obtained in terms of CPU time

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

GPU-friendly marching cubes.

Xie, Yongming.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 77-85).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Isosurfaces --- p.1Chapter 1.2 --- Graphics Processing Unit --- p.2Chapter 1.3 --- Objective --- p.3Chapter 1.4 --- Contribution --- p.3Chapter 1.5 --- Thesis Organization --- p.4Chapter 2 --- Marching Cubes --- p.5Chapter 2.1 --- Introduction --- p.5Chapter 2.2 --- Marching Cubes Algorithm --- p.7Chapter 2.3 --- Triangulated Cube Configuration Table --- p.12Chapter 2.4 --- Summary --- p.16Chapter 3 --- Graphics Processing Unit --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- History of Graphics Processing Unit --- p.19Chapter 3.2.1 --- First Generation GPU --- p.20Chapter 3.2.2 --- Second Generation GPU --- p.20Chapter 3.2.3 --- Third Generation GPU --- p.20Chapter 3.2.4 --- Fourth Generation GPU --- p.21Chapter 3.3 --- The Graphics Pipelining --- p.21Chapter 3.3.1 --- Standard Graphics Pipeline --- p.21Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.23Chapter 3.3.3 --- Vertex Processors --- p.25Chapter 3.3.4 --- Fragment Processors --- p.26Chapter 3.3.5 --- Frame Buffer Operations --- p.28Chapter 3.4 --- GPU CPU Analogy --- p.31Chapter 3.4.1 --- Memory Architecture --- p.31Chapter 3.4.2 --- Processing Model --- p.32Chapter 3.4.3 --- Limitation of GPU --- p.33Chapter 3.4.4 --- Input and Output --- p.34Chapter 3.4.5 --- Data Readback --- p.34Chapter 3.4.6 --- FramebufFer --- p.34Chapter 3.5 --- Summary --- p.35Chapter 4 --- Volume Rendering --- p.37Chapter 4.1 --- Introduction --- p.37Chapter 4.2 --- History of Volume Rendering --- p.38Chapter 4.3 --- Hardware Accelerated Volume Rendering --- p.40Chapter 4.3.1 --- Hardware Acceleration Volume Rendering Methods --- p.41Chapter 4.3.2 --- Proxy Geometry --- p.42Chapter 4.3.3 --- Object-Aligned Slicing --- p.43Chapter 4.3.4 --- View-Aligned Slicing --- p.45Chapter 4.4 --- Summary --- p.48Chapter 5 --- GPU-Friendly Marching Cubes --- p.49Chapter 5.1 --- Introduction --- p.49Chapter 5.2 --- Previous Work --- p.50Chapter 5.3 --- Traditional Method --- p.52Chapter 5.3.1 --- Scalar Volume Data --- p.53Chapter 5.3.2 --- Isosurface Extraction --- p.53Chapter 5.3.3 --- Flow Chart --- p.54Chapter 5.3.4 --- Transparent Isosurfaces --- p.56Chapter 5.4 --- Our Method --- p.56Chapter 5.4.1 --- Cell Selection --- p.59Chapter 5.4.2 --- Vertex Labeling --- p.61Chapter 5.4.3 --- Cell Indexing --- p.62Chapter 5.4.4 --- Interpolation --- p.65Chapter 5.5 --- Rendering Translucent Isosurfaces --- p.67Chapter 5.6 --- Implementation and Results --- p.69Chapter 5.7 --- Summary --- p.74Chapter 6 --- Conclusion --- p.76Bibliography --- p.7

Diamond-based models for scientific visualization

Hierarchical spatial decompositions are a basic modeling tool in a variety of application domains including scientific visualization, finite element analysis and shape modeling and analysis. A popular class of such approaches is based on the regular simplex bisection operator, which bisects simplices (e.g. line segments, triangles, tetrahedra) along the midpoint of a predetermined edge. Regular simplex bisection produces adaptive simplicial meshes of high geometric quality, while simplifying the extraction of crack-free, or conforming, approximations to the original dataset. Efficient multiresolution representations for such models have been achieved in 2D and 3D by clustering sets of simplices sharing the same bisection edge into structures called diamonds. In this thesis, we introduce several diamond-based approaches for scientific visualization. We first formalize the notion of diamonds in arbitrary dimensions in terms of two related simplicial decompositions of hypercubes. This enables us to enumerate the vertices, simplices, parents and children of a diamond. In particular, we identify the number of simplices involved in conforming updates to be factorial in the dimension and group these into a linear number of subclusters of simplices that are generated simultaneously. The latter form the basis for a compact pointerless representation for conforming meshes generated by regular simplex bisection and for efficiently navigating the topological connectivity of these meshes. Secondly, we introduce the supercube as a high-level primitive on such nested meshes based on the atomic units within the underlying triangulation grid. We propose the use of supercubes to associate information with coherent subsets of the full hierarchy and demonstrate the effectiveness of such a representation for modeling multiresolution terrain and volumetric datasets. Next, we introduce Isodiamond Hierarchies, a general framework for spatial access structures on a hierarchy of diamonds that exploits the implicit hierarchical and geometric relationships of the diamond model. We use an isodiamond hierarchy to encode irregular updates to a multiresolution isosurface or interval volume in terms of regular updates to diamonds. Finally, we consider nested hypercubic meshes, such as quadtrees, octrees and their higher dimensional analogues, through the lens of diamond hierarchies. This allows us to determine the relationships involved in generating balanced hypercubic meshes and to propose a compact pointerless representation of such meshes. We also provide a local diamond-based triangulation algorithm to generate high-quality conforming simplicial meshes

Distributed smart camera network for safety and security

Current CCTV surveillance solutions are generally retrospective tools. Because real time use of CCTV requires human monitors to view a potentially exorbitant number of video feeds, CCTV is usually only useful after an incident has occurred. However, new technologies are making it possible for machines to perform some tasks that previously required a human monitor. The proposed project seeks to augment existing CCTV systems with behavioral analytics. The system uses a series of cameras, FPGAs, and computers to track object movement throughout a facility. This information is used to build a model of normal movement. Object movements are compared against this model and any ones that diverge from the model are flagged for review by security personnel

Counting cases in substitope algorithms

We describe how to count the cases that arise in a family of visualization techniques, including Marching Cubes, Sweeping Simplices, Contour Meshing, Interval Volumes, and Separating Surfaces. Counting the cases is the first step toward developing a generic visualization algorithm to produce substitopes ( geometric substitutions of polytopes). We demonstrate the method using "GAP," a software system for computational group theory. The case-counts are organized into a table that provides a taxonomy of members of the family; numbers in the table are derived from actual lists of cases, which are computed by our methods. The calculations confirm previously reported case-counts for four dimensions that are too large to check by hand and predict the number of cases that will arise in substitope algorithms that have not yet been invented. We show how Polya theory produces a closed-form upper bound on the case counts.Publisher PDFPeer reviewe