Light Detection And Ranging (LiDAR) technology provides the means for fast and accurate acquisition of geospatial data. Quality control of the derived data is an important process for verifying whether the requirements of the scanning mission have been met. Point density presents one of the most important factors for evaluating LiDAR data. This paper presents a new method for evaluating the point density of LiDAR data through by applying methods of computational geometry. This method treats the LiDAR scan with regard to terrain characteristics and divides it into those areas that can be scanned and those that prevent quality scanning and produce weak returns. Point density evaluation is performed using the Voronoi diagram, which allows efficient extraction of actual LiDAR point density.
COBISS.SI-ID: 18724374
This paper introduces a new approach for lossless chain code compression. Firstly, the chain codes are converted into the binary stream, dependent on the input chain code. Then, the compression is done using three modes: RLE_0, LZ77_0 and COPY. RLE_0 compresses the runs of the 0-bits, LZ77_0 is a simplified version of LZ77 and handles the repetitions within the bit stream, whilst COPY is an escape mode used, when the other two methods are unsuccessful. This method has been tested on the Freeman chain code in eight and four directions, the Vertex chain code, the Three OrThogonal chain code, and the Normalized angle difference chain code. The experiments confirmed better compression ratios on various benchmark shapes in comparison to the state-of-the-art lossless chain code compression methods.
COBISS.SI-ID: 18414102
An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale realworld networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities.
COBISS.SI-ID: 21987592