Hyper-Fractal Analysis v04: Implementation of a fuzzy box-counting algorithm for image analysis of artistic works
| Main Author: | CPC, Mendeley |
|---|---|
| Other Authors: | Grossu, I.V., El-Shamali, S.A. |
| Format: | Dataset |
| Terbitan: |
Mendeley
, 2013
|
| Subjects: | |
| Online Access: |
https:/data.mendeley.com/datasets/g7gdbdchw7 |
| ctrlnum |
0.17632-g7gdbdchw7.1 |
|---|---|
| fullrecord |
<?xml version="1.0"?>
<dc><creator>CPC, Mendeley</creator><title>Hyper-Fractal Analysis v04: Implementation of a fuzzy box-counting algorithm for image analysis of artistic works </title><publisher>Mendeley</publisher><description>Abstract
This work presents a new version of a Visual Basic 6.0 application for estimating the fractal dimension of images and 4D objects (Grossu et al. 2013 [1]). Following our attempt of investigating artistic works by fractal analysis of craquelure, we encountered important difficulties in filtering real information from noise. In this context, trying to avoid a sharp delimitation of “black” and “white” pixels, we implemented a fuzzy box-counting algorithm.
Title of program: Hyper-Fractal Analysis v04
Catalogue Id: AEEG_v4_0
Nature of problem
Estimating the fractal dimension of images
Versions of this program held in the CPC repository in Mendeley Data
AEEG_v1_0; Fractal Analysis v01; 10.1016/j.cpc.2009.05.015
AEEG_v2_0; Fractal Analysis v02; 10.1016/j.cpc.2009.12.005
AEEG_v3_0; Hyper-Fractal Analysis (Frcatal Analysis v03); 10.1016/j.cpc.2012.11.018
AEEG_v4_0; Hyper-Fractal Analysis v04; 10.1016/j.cpc.2013.02.026
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)</description><subject>Computer Graphics</subject><subject>Computational Physics</subject><contributor>Grossu, I.V.</contributor><contributor>El-Shamali, S.A.</contributor><type>Other:Dataset</type><identifier>10.17632/g7gdbdchw7.1</identifier><rights>Computer Physics Communications Journal Licence</rights><rights>https://www.elsevier.com/about/policies/open-access-licenses/elsevier-user-license/cpc-license/</rights><relation>https:/data.mendeley.com/datasets/g7gdbdchw7</relation><date>2013-07-01T11:00:00Z</date><recordID>0.17632-g7gdbdchw7.1</recordID></dc>
|
| format |
Other:Dataset Other |
| author |
CPC, Mendeley |
| author2 |
Grossu, I.V. El-Shamali, S.A. |
| title |
Hyper-Fractal Analysis v04: Implementation of a fuzzy box-counting algorithm for image analysis of artistic works |
| publisher |
Mendeley |
| publishDate |
2013 |
| topic |
Computer Graphics Computational Physics |
| url |
https:/data.mendeley.com/datasets/g7gdbdchw7 |
| contents |
Abstract
This work presents a new version of a Visual Basic 6.0 application for estimating the fractal dimension of images and 4D objects (Grossu et al. 2013 [1]). Following our attempt of investigating artistic works by fractal analysis of craquelure, we encountered important difficulties in filtering real information from noise. In this context, trying to avoid a sharp delimitation of “black” and “white” pixels, we implemented a fuzzy box-counting algorithm.
Title of program: Hyper-Fractal Analysis v04
Catalogue Id: AEEG_v4_0
Nature of problem
Estimating the fractal dimension of images
Versions of this program held in the CPC repository in Mendeley Data
AEEG_v1_0; Fractal Analysis v01; 10.1016/j.cpc.2009.05.015
AEEG_v2_0; Fractal Analysis v02; 10.1016/j.cpc.2009.12.005
AEEG_v3_0; Hyper-Fractal Analysis (Frcatal Analysis v03); 10.1016/j.cpc.2012.11.018
AEEG_v4_0; Hyper-Fractal Analysis v04; 10.1016/j.cpc.2013.02.026
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019) |
| id |
IOS7969.0.17632-g7gdbdchw7.1 |
| institution |
Universitas Islam Indragiri |
| affiliation |
onesearch.perpusnas.go.id |
| institution_id |
804 |
| institution_type |
library:university library |
| library |
Teknologi Pangan UNISI |
| library_id |
2816 |
| collection |
Artikel mulono |
| repository_id |
7969 |
| city |
INDRAGIRI HILIR |
| province |
RIAU |
| shared_to_ipusnas_str |
1 |
| repoId |
IOS7969 |
| first_indexed |
2020-04-08T08:25:40Z |
| last_indexed |
2020-04-08T08:25:40Z |
| recordtype |
dc |
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1686587738047905792 |
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17.538404 |