Entropy reduction via simplified image contourization

The process of contourization is presented which converts a raster image into a set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimizes noticeable artifacts in the simplified image

Location-aware alert system for mobile devices

Being able to react fast to campaign events such as missing persons or disaster preventions, is of paramount importance. In these situations narrowing down the search area to a targeted and accurate location is imperative. Nowadays, modern mobile devices have the location awareness capabilities that can be used to determine the users Global Positioning System (GPS) coordinates. However in order to determine if a user is located within a specific area, complex floating point calculations are required. Moreover if the area is determined by a polygon, this calculation is further complicated. In this paper we propose a novel algorithm which makes use of spatial indices to determine if a mobile is located within a predefined polygon shape area. The algorithm determines the optimal length of the spatial index such as to ensure accuracy-processing time-memory trade-off. We build a prototype system, using free and open source software, to deliver alerts to mobile devices within a predetermined geographical area. The system is assessed in terms of accuracy, processing time and memory usage

Quadtrees, transforms and image coding

Transforms and quadtrees are both methods of representing information in an image in terms of the presence of information at differing length scales. This paper presents a mathematical relationship between these two approaches to describing images in the particular case when Walsh transforms are used. Furthermore, both methods have been used for the compression of images for transmission. This paper notes that under certain circumstances, quadtree compression produces identical results to Walsh transform coding, but requires less computational effort to do so. Remarks are also made about the differences between these approaches

GPU point list generation through histogram pyramids

Image Pyramids are frequently used in porting non-local algorithms to graphics hardware. A Histogram pyramid (short: HistoPyramid), a special version of image pyramid, sums up the number of active entries in a 2D image hierarchically. We show how a HistoPyramid can be utilized as an implicit indexing data structure, allowing us to convert a sparse matrix into a coordinate list of active cell entries (a point list) on graphics hardware . The algorithm reduces a highly sparse matrix with N elements to a list of its M active entries in O(N) + M (log N) steps, despite the restricted graphics hardware architecture. Applications are numerous, including feature detection, pixel classification and binning, conversion of 3D volumes to particle clouds and sparse matrix compression

0018/2009 - Indexação em Bancos de Dados Espaciais

Um banco de dados espacial é um banco de dados que deve ter a capacidade de lidar com informações relacionadas ao espaço. Para tanto, tipos de dados específicos para este contexto são essenciais na modelagem dos objetos espaciais e na sua implementação. Além disso, operações e consultas no banco espacial devem ser eficientes e capazes de interagir com esses tipos específicos. Aumentar esta eficiência é um dos incentivos para a criação de índices em banco de dados. No caso dos bancos de dados espaciais, os índices devem considerar características próprias do contexto, o que torna a criação de índices tão ou mais complexa que a mesma tarefa em bancos relacionais. O desempenho dos bancos espaciais é influenciado negativamente pelo aumento do uso e do volume de dados, o que aumenta a importância dos índices. Métodos usados para indexação espacial se utilizam de árvores de busca, onde os ramos representam divisões do espaço para a construção de dos índices. Outra abordagem utilizada é a aproximação (ou assinatura) do resultado para permitir que operações usando o aspecto espacial dos objetos, sejam realizados com maior velocidade. Este trabalho apresenta um levantamento da literatura sobre o tema de indexação em bancos de dados espaciais

Quadtrees, transforms and image coding

Transforms and quadtrees are both methods of representing information in an image in terms of the presence of information at differing length scales. This paper presents a mathematical relationship between these two approaches to describing images in the particular case when Walsh transforms are used. Furthermore, both methods have been used for the compression of images for transmission. This paper notes that under certain circumstances, quadtree compression produces identical results to Walsh transform coding, but requires less computational effort to do so. Remarks are also made about the differences between these approaches

Bézier Method For Image Processing

This project concerns about Bézier method in Computer Aided GeometricDesign (CAGD) involving Bézier Curve and Bézier Surface which widely related to the other theorem and method. The aim of this project is to introduce the basic of Bézier method and then generate the Bézier curves, Bézier surfaces, theory and properties and develop Bézier method in image processing application specifically image compression by using MATLAB

Space-Efficient Representations of Raster Time Series

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast systems to monitoring of forest degradation or soil contamination. Many different sensors are generating this type of data, which makes such analyses possible, but also challenges the technological capacity to store and retrieve the data. In this work, we propose a space-efficient representation of raster time series that is based on Compact Data Structures (CDS). Our method uses a strategy of snapshots and logs to represent the data, in which both components are represented using CDS. We study two variants of this strategy, one with regular sampling and another one based on a heuristic that determines at which timestamps should the snapshots be created to reduce the space redundancy. We perform a comprehensive experimental evaluation using real datasets. The results show that the proposed strategy is competitive in space with alternatives based on pure data compression, while providing much more efficient query times for different types of queries.The data used in this study were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC). Funding: CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). This work was also supported by Xunta de Galicia/FEDER-UE under Grants [IG240.2020.1.185; IN852A 2018/14]; Ministerio de Ciencia, Innovación y Universidades under Grants [TIN2016-78011-C4-1-R; RTC-2017-5908-7; PID2019- 105221RB-C41/AEI/10.13039/501100011033]; ANID - Millennium Science Initiative Program - Code ICN17_002; Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo (CYTED) [Grant No. 519RT0579]Xunta de Galicia; ED431G 2019/01Xunta de Galicia; IG240.2020.1.185Xunta de Galicia; IN852A 2018/14Chile. Agencia Nacional de Investigación y Desarrollo; ICN17_00

Incremental Construction of Generalized Voronoi Diagrams on Pointerless Quadtrees

In robotics, Generalized Voronoi Diagrams (GVDs) are widely used by mobile robots to represent the spatial topologies of their surrounding area. In this paper we consider the problem of constructing GVDs on discrete environments. Several algorithms that solve this problem exist in the literature, notably the Brushfire algorithm and its improved versions which possess local repair mechanism. However, when the area to be processed is very large or is of high resolution, the size of the metric matrices used by these algorithms to compute GVDs can be prohibitive. To address this issue, we propose an improvement on the current algorithms, using pointerless quadtrees in place of metric matrices to compute and maintain GVDs. Beyond the construction and reconstruction of a GVD, our algorithm further provides a method to approximate roadmaps in multiple granularities from the quadtree based GVD. Simulation tests in representative scenarios demonstrate that, compared with the current algorithms, our algorithm generally makes an order of magnitude improvement regarding memory cost when the area is larger than 210×210. We also demonstrate the usefulness of the approximated roadmaps for coarse-to-fine pathfinding tasks