scholarly journals The construction of local convex hull on the task of pattern recognition

2021 ◽  
Vol 186 ◽  
pp. 360-365
Author(s):  
Kamilov Mirzoyan ◽  
Hudayberdiev Mirzaakbar ◽  
Khamroev Alisher
Keyword(s):  
Pattern Recognition ◽  
Convex Hull ◽  
Local Convex

scholarly journals Local convex hull support and boundary estimation

2016 ◽  
Vol 147 ◽  
pp. 82-101 ◽  
Author(s):  
C. Aaron ◽  
O. Bodart
Keyword(s):  
Convex Hull ◽  
Boundary Estimation ◽  
Local Convex

scholarly journals Differences in evaluation of three different approaches in home range sizes of red deer Cervus elaphus in Western Carpathians / Rozdiely vo vyhodnotení veľkosti domovských okrskov jeleňa lesného Cervus elaphus v Západných Karpatoch tromi rôznymi prístupmi

2015 ◽  
Vol 61 (1) ◽  
pp. 12-18
Author(s):  
Alfadil Mohammed Abdelrahman Adam ◽  
Rudolf Kropil ◽  
Viliam Pichler
Keyword(s):  
Home Range ◽  
Convex Hull ◽  
Cervus Elaphus ◽  
Red Deer ◽  
Home Range Size ◽  
Range Size ◽  
Western Carpathians ◽  
Seasonal Effect ◽  
Minimum Convex Polygon ◽  
Local Convex

Abstract The aim of this study was to evaluate differences using three different approaches in home range sizes of selected male individuals of red deer (Cervus elaphus). This study was conducted in the Kremnica Mountains (the Western Carpathians) located in central Slovakia. The study included data from three individuals, collared and tracked by using the VHF (Very High Frequency) telemetry. The data were evaluated within three different seasons (winter, summer and rut). For the measuring of home range sizes three methods were used: Minimum Convex Polygon (MCP), Kernel Home Range (KHR) and Local Convex Hull (LoCoH). The seasonal effect on home range size was analyzed by using the analysis of variance (ANOVA) two and three main effects. The study showed differences in home range sizes and core areas in red deer population. The migrant individual had a lager home range size with used methods. The differences occurred between total seasonal home ranges, but statistically not significant. The home range created by Local Convex Hull was significantly smaller than the home range created by Kernel Home Range.

scholarly journals The Time Local Convex Hull method as a tool for assessing responses of fauna to habitat restoration: a case study using the perentie (Varanus giganteus : Reptilia : Varanidae)

2019 ◽  
Vol 67 (1) ◽  
pp. 27 ◽  
Author(s):  
Sophie L. Cross ◽  
Sean Tomlinson ◽  
Michael D. Craig ◽  
Philip W. Bateman
Keyword(s):  
Home Range ◽  
Convex Hull ◽  
Habitat Restoration ◽  
Single Point ◽  
Habitat Change ◽  
Gps Tracking ◽  
Behavioural Responses ◽  
Local Convex ◽  
Convex Hull Method

Understanding the behavioural responses of animals to habitat change is vital to their conservation in landscapes undergoing restoration. Studies of animal responses to habitat restoration typically assess species presence/absence; however, such studies may be restricted in their ability to show whether restoration is facilitating the return of self-sustaining and functional fauna populations. We present a case study using VHF/GPS tracking of a young adult perentie (Varanus giganteus), to demonstrate the range of applications of the Time Local Convex Hull method of home-range construction in analysing the behavioural responses of fauna to habitat change and restoration. Presence/absence studies provide single point locations of an animal, and the Minimum Convex Polygon method provides an invariant estimate of habitat use across the whole home range. However, the Time Local Convex Hull method provides a useful method for assessing movement and behavioural responses of fauna to habitat change and restoration, and the specific habitat requirements for the long-term support of populations. The breadth and multidimensionality of data generated indicates strongly that understanding the complex interactions between animals and their environment is fundamental to their conservation in the face of ever-increasing rates of human-induced habitat change and degradation.

Estimating utilization distributions with kernel versus local convex hull methods

2011 ◽  
Vol 75 (2) ◽  
pp. 413-422 ◽  
Author(s):  
Nathanael I. Lichti ◽  
Robert K. Swihart
Keyword(s):  
Convex Hull ◽  
Local Convex ◽  
Utilization Distributions

Recognizing Molecular Structural Features by Pattern Recognition Techniques

2021 ◽  
Author(s):  
Qing Lu ◽  
Wensheng Bian
Keyword(s):  
Pattern Recognition ◽  
Convex Hull ◽  
Structural Features ◽  
Machine Learning Techniques ◽  
Pattern Recognition Techniques ◽  
Blob Detection ◽  
Learning Techniques ◽  
Adsorption Processes ◽  
Non Covalent Interactions ◽  
Covalent Interactions

Abstract Recognition of molecular structural features is one of the most attractive fields in chemistry, especially when combining with machine learning techniques. Pattern recognition techniques are straightforward in recognizing graphic features, but little attention was given to recognize molecular structural features. In this work, we propose a new method taking advantage of pattern recognition techniques to analyze structural features and obtain novel chemical insights. Specifically, the cluster analysis is presented to recognize structural features, which provides an alternative to the most widely used root mean square deviation (RMSD) method and the recently proposed blob detection method. Based on this, the convex hull of the molecule is constructed. The convex hull of molecules is highly appealing in the sense that one can introduce established theorems and properties from other disciplines into chemistry. Novel molecular descriptors based on convex hulls can be defined and show encouraging results, especially in providing new insights in understanding non-covalent interactions, adsorption processes, etc.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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