This volume contains research articles and surveys presented at Ianfest-66, a conference on space efficient data structures, streams and algorithms held on August 15th and 16th, 2013 at the University of Waterloo, Canada. The conference was held to celebrate Ian Munro's 66th birthday. Just as Ian's interests, the articles in this volume encompass a spectrum of areas including sorting, searching, selection and several types of, and topics in, data structures including space efficient ones.
C.01 Editorial board of a foreign/international collection of papers/book
COBISS.SI-ID: 10147668This dissertation considers two new methods for automatic generation of digital terrain models from LiDAR data. The first method iterates a thin platespline interpolated surface towards the ground, while pointsć residuals from the surface are inspected at each iteration, with a gradually decreasing structural element. Top-hat transformation is used to enhance discontinuities caused by surface objects. Finally, parameter-free ground point filtering is achieved by automatic thresholding, based on a standard deviation. The experiments show that this method correctly determines DTM even in those casesof difficult terrain features. The expected accuracy of ground point determination on those datasets commonly used in practice today is over 96%, while the average total error produced on the ISPRS benchmark dataset is under6%. The second method uses an adaptive morphological filter, where the size of the structural element is defined by the distance of a point from itćsnearest edge. The input data is arranged into a grid and compass edge detection based on the Sobel operator is applied for edge extraction. Morphological region-filling is used in order to segment grid-cells into foreground and background regions, while the distance transformation of the foreground regions defines the size of the structural element for each foreground grid-cell. Finally, LiDAR point-filtering is achieved using adaptive top-hat transformation, followed by a constant thresholding. As confirmed by experiments, the average CPU execution time decreases by more than 94% compared to the first method, while the accuracy improves by nearly 20% on low-density datasets, and by nearly 30% on high-density datasets.
D.09 Tutoring for postgraduate students
COBISS.SI-ID: 16270870In some embodiments, the present invention provides a method for compressing three dimensional point data. The method includes receiving a dataset to be compressed, the dataset including a plurality of data points in a sequence, the data points each including at least four types of attribute values, the attribute value types including an X-coordinate value type, a Y-coordinate value type, a Z-coordinate value type, and at least one associated scalar value type; applying predictive coding to a sequence of attribute values of a same type from the dataset to generate a sequence of prediction errors for a given attribute value type; applying variable length coding to the sequence of prediction errors to generate byte-streams of variable length codes; and compressing the byte-streams of variable length codes using entropy coding. In some other embodiments, the present invention provides a medium including a non-transitory computer-readable medium having computer-executable instructions adapted to cause a computer to receive a dataset to be compressed, the dataset including a plurality of data points in a sequence, the data points each including at least four types of attribute values, the attribute value types including an X-coordinate value type, a Y-coordinate value type, a Z-coordinate value type, and at least one associated scalar value type; apply predictive coding to a sequence of attribute values of a same type from the dataset to generate a sequence of prediction errors for a given attribute value type; apply variable length coding to the sequence of prediction errors to generate byte-streams of variable length codes; and compress the byte-streams of variable length codes using entropy coding. In yet other embodiments, the present invention provides a computer programmed to compress three dimensional point data. The computer includes a processor; and a memory coupled to the processor and operable to store computer-executable instructions adapted to cause the computer to receive a dataset to be compressed, the dataset including a plurality of data points in a sequence, the data points each including at least four types of attribute values, the attribute value types including an X-coordinate value type, a Y-coordinate value type, a Z-coordinate value type, and at least one associated scalar value type; apply predictive coding to a sequence of attribute values of a same type from the dataset to generate a sequence of prediction errors for a given attribute value type; apply variable length coding to the sequence of prediction errors to generate byte-streams of variable length codes; and compress the byte-streams of variable length codes using entropy coding. Numerous other aspects are provided in accordance with these and other aspects of the invention. Other features and aspects of the present invention will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawings.
F.32 International patent
COBISS.SI-ID: 14709270The lecture intended for The Chamber of Craft and Small Business of Slovenia is an overview of advanced remote sensing technologies and their innovation potential, with special emphasis on Light Detection and Ranging (LiDAR). First, a brief summary of the technological characteristics of state-of-the-art remote sensing technologies are given, including satellite imagery, RADAR, and LiDAR. Then we describe the current advances being made in the area of data visualization, compression, and organization along with data processing and analytics. Finally, several innovative solutions already developed based this remote sensing technologies in Slovenia are presented, including solar potential estimation, monitoring of tree-growth, and mapping of industrial infrastructure.
F.18 Transfer of new know-how to direct users (seminars, fora, conferences)
COBISS.SI-ID: 16706070The invention relates to the lossy compression of LAS files which store large amounts of data obtained by remote sensing scanners (LiDAR). The procedure presented by this invention, consists of four steps: 1) pixel removal within the areas with a higher sampling rate, 2) the movement of the other points within user-specified limits, 3) of variable-length encoding, and 4) of the arithmetic coding. As shown by experimentation, the invention improves existing lossy LAS file compression methods.
F.33 Slovenian patent
COBISS.SI-ID: 17217302