An Image Analysis Approach to the Calligraphy of Books

08/24/2017
by   Henrique F. de Arruda, et al.
0

Text network analysis has received increasing attention as a consequence of its wide range of applications. In this work, we extend a previous work founded on the study of topological features of mesoscopic networks. Here, the geometrical properties of visualized networks are quantified in terms of several image analysis techniques and used as subsidies for authorship attribution. It was found that the visual features account for performance similar to that achieved by using topological measurements. In addition, the combination of these two types of features improved the performance.

READ FULL TEXT

page 4

page 7

research
07/10/2020

Learning Local Complex Features using Randomized Neural Networks for Texture Analysis

Texture is a visual attribute largely used in many problems of image ana...
research
07/09/2022

Rethinking Persistent Homology for Visual Recognition

Persistent topological properties of an image serve as an additional des...
research
10/18/2019

A Topological "Reading" Lesson: Classification of MNIST using TDA

We present a way to use Topological Data Analysis (TDA) for machine lear...
research
05/31/2023

Bayesian Image Analysis in Fourier Space

Bayesian image analysis has played a large role over the last 40+ years ...
research
06/24/2021

Topological Semantic Mapping by Consolidation of Deep Visual Features

Many works in the recent literature introduce semantic mapping methods t...
research
02/18/2022

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

With the acceleration of urbanization and living standards, microorganis...

Please sign up or login with your details

Forgot password? Click here to reset