scholarly journals Graph theory and mean shift segmentation based classification of building facades

2011 ◽  
Author(s):  
Beril Sirmacek
Keyword(s):  
Graph Theory ◽  
Mean Shift ◽  
Building Facades

scholarly journals Rule based Classification of Graph Theory Concepts by Use Case Analysis

2016 ◽  
Vol 9 (48) ◽  
Author(s):  
Anuja Bokhare ◽  
P. S. Metkewar ◽  
R. S. Walse
Keyword(s):  
Graph Theory ◽  
Case Analysis ◽  
Use Case ◽  
Rule Based

scholarly journals Graph Theory-Based Characterization and Classification of Household Photovoltaics

2021 ◽  
Vol 11 (22) ◽  
pp. 10999
Author(s):  
Jesús M. Ceresuela ◽  
Daniel Chemisana ◽  
Nacho López
Keyword(s):  
Graph Theory ◽  
Power Production ◽  
Expected Value ◽  
Maximum Power ◽  
Solar Panels ◽  
Zero Energy ◽  
Photovoltaic Panel ◽  
Clear Goal ◽  
Zero Energy Buildings

With the clear goal of improving photovoltaic (PV) technology performance towards nearly-zero energy buildings, a graph theory-based model that characterizes photovoltaic panel structures is developed. An algorithm to obtain all possible configurations of a given number of PV panels is presented and the results are exposed for structures using 3 to 7 panels. Two different classifications of all obtained structures are carried out: the first one regarding the maximum power they can produce and the second according to their capability to produce energy under a given probability that the solar panels will fail. Finally, both classifications are considered simultaneously through the expected value of power production. This creates structures that are, at the same time, reliable and efficient in terms of production. The parallel associations turn out to be optimal, but some other less expected configurations prove to be rated high.

scholarly journals Classification of mini-dimmings associated with extreme ultraviolet eruptions by using graph theory

2016 ◽  
Vol 16 (2) ◽  
pp. 217-223
Author(s):  
S Bazargan ◽  
H Safari ◽  
H Kaashisaaz ◽  
◽  
◽  
...  
Keyword(s):  
Graph Theory ◽  
Extreme Ultraviolet

Acoustic classification of building façades using statistical methods

2021 ◽  
Vol 172 ◽  
pp. 107653
Author(s):  
Aleksandras Jagniatinskis ◽  
Boris Fiks ◽  
Marius Mickaitis
Keyword(s):  
Statistical Methods ◽  
Building Facades ◽  
Acoustic Classification

An investigation into colour combination in paintings via graph theory

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Samaneh Jolany Vangah ◽  
Yousef Jamali ◽  
Mozaffar Jamali
Keyword(s):  
Image Processing ◽  
Graph Theory ◽  
Network Analysis ◽  
Visual Arts ◽  
Digital Image ◽  
Degree Distribution ◽  
Network Science ◽  
Node Centrality ◽  
Classification Of Images

Abstract In visual arts, painting is deeply reliant on the colour combination for its impact, depth and emotion. Recently, many studies have focused on image processing, regarding identification and classification of images, using some colour features such as saturation, hue, luminance and so forth. This study aims to delve into some of the painting styles from the perspective of graph theory and network science. We compared a number of famous paintings to find out the likely pattern that an artist uses for colour combination and juxtaposition. To achieve this aim, the digital image of a painting is converted to a graph where each vertex represents one of the painting’s colours. In this graph, two vertices would be adjacent if and only if the two relative colours could be found in at least two adjacent pixels in the digital image. Among the several tools for network analysis, clustering, node centrality and degree distribution are used. Outcomes showed that artists unconsciously are following subtle mathematical rules to reach harmony and coordination in their work.

scholarly journals Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1218 ◽  
Author(s):  
Julia Maschler ◽  
Clement Atzberger ◽  
Markus Immitzer
Keyword(s):  
Random Forest ◽  
Scots Pine ◽  
Tree Species ◽  
Near Infrared ◽  
Mean Shift ◽  
Hyperspectral Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Tree Crowns

Knowledge of the distribution of tree species within a forest is key for multiple economic and ecological applications. This information is traditionally acquired through time-consuming and thereby expensive field work. Our study evaluates the suitability of a visible to near-infrared (VNIR) hyperspectral dataset with a spatial resolution of 0.4 m for the classification of 13 tree species (8 broadleaf, 5 coniferous) on an individual tree crown level in the UNESCO Biosphere Reserve ‘Wienerwald’, a temperate Austrian forest. The study also assesses the automation potential for the delineation of tree crowns using a mean shift segmentation algorithm in order to permit model application over large areas. Object-based Random Forest classification was carried out on variables that were derived from 699 manually delineated as well as automatically segmented reference trees. The models were trained separately for two strata: small and/or conifer stands and high broadleaf forests. The two strata were delineated beforehand using CHM-based tree height and NDVI. The predictor variables encompassed spectral reflectance, vegetation indices, textural metrics and principal components. After feature selection, the overall classification accuracy (OA) of the classification based on manual delineations of the 13 tree species was 91.7% (Cohen’s kappa (κ) = 0.909). The highest user’s and producer’s accuracies were most frequently obtained for Weymouth pine and Scots Pine, while European ash was most often associated with the lowest accuracies. The classification that was based on mean shift segmentation yielded similarly good results (OA = 89.4% κ = 0.883). Based on the automatically segmented trees, the Random Forest models were also applied to the whole study site (1050 ha). The resulting tree map of the study area confirmed a high abundance of European beech (58%) with smaller amounts of oak (6%) and Scots pine (5%). We conclude that highly accurate tree species classifications can be obtained from hyperspectral data covering the visible and near-infrared parts of the electromagnetic spectrum. Our results also indicate a high automation potential of the method, as the results from the automatically segmented tree crowns were similar to those that were obtained for the manually delineated tree crowns.

A graph-based active learning method for classification of remote sensing images

2018 ◽  
Vol 16 (04) ◽  
pp. 1850023
Author(s):  
Lian-Zhi Huo ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
Active Learning ◽  
Image Classification ◽  
Selection Procedure ◽  
Experimental Tests ◽  
Maximization Problem ◽  
Support Vector ◽  
Training Samples

Remote sensing (RS) technology provides essential data for monitoring the Earth. To fully utilize the data, image classification is often needed to convert data to information. The success of image classification methods greatly depends on the quality and quantity of training samples. To effectively select more informative training samples, this paper proposes a new active learning (AL) technique for classification of remote sensing (RS) images based on graph theory. A new diversity criterion is proposed based on geometrical features of the support vector machines (SVM) outputs. The diversity selection procedure is converted to the densest k-subgraph [Formula: see text] maximization problem in graph theory. The [Formula: see text] maximization problem is solved by a greedy algorithm. The proposed technique is compared with competing methods adopted in RS community. Experimental tests are performed on very high resolution (VHR) multispectral and hyperspectral images. Experimental results demonstrate that the proposed technique leads to comparable or even better classification accuracies with respect to competing methods on the two datasets.

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