Geocoded data structures and their applications to Earth science investigations

A geocoded data structure is a means for digitally representing a geographically referenced map or image. The characteristics of representative cellular, linked, and hybrid geocoded data structures are reviewed. The data processing requirements of Earth science projects at the Goddard Space Flight Center and the basic tools of geographic data processing are described. Specific ways that new geocoded data structures can be used to adapt these tools to scientists' needs are presented. These include: expanding analysis and modeling capabilities; simplifying the merging of data sets from diverse sources; and saving computer storage space

TetSplat: Real-time Rendering and Volume Clipping of Large Unstructured Tetrahedral Meshes

We present a novel approach to interactive visualization and exploration of large unstructured tetrahedral meshes. These massive 3D meshes are used in mission-critical CFD and structural mechanics simulations, and typically sample multiple field values on several millions of unstructured grid points. Our method relies on the pre-processing of the tetrahedral mesh to partition it into non-convex boundaries and internal fragments that are subsequently encoded into compressed multi-resolution data representations. These compact hierarchical data structures are then adaptively rendered and probed in real-time on a commodity PC. Our point-based rendering algorithm, which is inspired by QSplat, employs a simple but highly efficient splatting technique that guarantees interactive frame-rates regardless of the size of the input mesh and the available rendering hardware. It furthermore allows for real-time probing of the volumetric data-set through constructive solid geometry operations as well as interactive editing of color transfer functions for an arbitrary number of field values. Thus, the presented visualization technique allows end-users for the first time to interactively render and explore very large unstructured tetrahedral meshes on relatively inexpensive hardware

The Representation of symmetric patterns using the quadtree data structure

Hierarchical data structures for image representation have been widely explored in recent years. These data structures are based on the principle of recursive decomposition of an image region. The most commonly mentioned picture data structure for two-dimensional data is referred to as a quadtree . The purpose of this thesis is to investigate the use of a general quadtree scheme as a means of representing symmetric images. Specifically, images are generated according to the rules of selected two-dimensional plane symmetry groups

Linkless octree using multi-level perfect hashing

The standard C/C++ implementation of a spatial partitioning data structure, such as octree and quadtree, is often inefficient in terms of storage requirements particularly when the memory overhead for maintaining parent-to-child pointers is significant with respect to the amount of actual data in each tree node. In this work, we present a novel data structure that implements uniform spatial partitioning without storing explicit parent-to-child pointer links. Our linkless tree encodes the storage locations of subdivided nodes using perfect hashing while retaining important properties of uniform spatial partitioning trees, such as coarse-to-fine hierarchical representation, efficient storage usage, and efficient random accessibility. We demonstrate the performance of our linkless trees using image compression and path planning examples.postprin

Very low complexity convolutional neural network for quadtree structures

© 2018 Australasian Robotics and Automation Association. All rights reserved. In this paper, we present a Very Low Complexity Convolutional Neural Network (VLC-CNN) for the purpose of generating quadtree data structures for image segmentation. The use of quadtrees to encode images has applications including video encoding and robotic perception, with examples including the Coding Tree Unit in the High Efficiency Video Coding (HEVC) standard and Occupancy Grid Maps (OGM) as environment representations with variable grid-size. While some methods for determining quadtree structures include brute-force algorithms or heuristics, this paper describes the use of a Convolutional Neural Network (CNN) to predict the quadtree structure. CNNs traditionally require substantial computational and memory resources to operate, however, VLC-CNN exploits downsampling and integer-only quantised arithmetic to achieve minimal complexity. Therefore, VLC-CNN's minimal design makes it feasible for implementation in realtime or memory-constrained processing applications

Low-complexity wavelet-based scalable image & video coding for home-use surveillance

We study scalable image and video coding for the surveillance of rooms and personal environments based on inexpensive cameras and portable devices. The scalability is achieved through a multi-level 2D dyadic wavelet decomposition featuring an accurate low-cost integer wavelet implementation with lifting. As our primary contribution, we present a modification to the SPECK wavelet coefficient encoding algorithm to significantly improve the efficiency of an embedded system implementation. The modification consists of storing the significance of all quadtree nodes in a buffer, where each node comprises several coefficients. This buffer is then used to efficiently construct the code with minimal and direct memory access. Our approach allows efficient parallel implementation on multi-core computer systems and gives a substantial reduction of memory access and thus power consumption. We report experimental results, showing an approximate gain factor of 1,000 in execution time compared to a straightforward SPECK implementation, when combined with code optimization on a common digital signal processor. This translates to 75 full color 4CIF 4:2:0 encoding cycles per second, clearly demonstrating the realtime capabilities of the proposed modification