activity
20202025
most citedA Comprehensive Taxonomy of Cellular Automata

19 citations · 27 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2025

VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token Encodings

Leonardo Scabini, Kallil M. Zielinski, Emir Konuk +4

Texture recognition has recently been dominated by ImageNet-pre-trained deep Convolutional Neural Networks (CNNs), with specialized modifications and feature engineering required t…

cs.CV2024★ 3 cited

A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis

Leonardo Scabini, Andre Sacilotti, Kallil M. Zielinski +3

Texture, a significant visual attribute in images, has been extensively investigated across various image recognition applications. Convolutional Neural Networks (CNNs), which have…

cs.CV2024

Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion

Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas +5

In recent years, we have seen many advancements in wood species identification. Methods like DNA analysis, Near Infrared (NIR) spectroscopy, and Direct Analysis in Real Time (DART)…

nlin.CG2024★ 19 cited

A Comprehensive Taxonomy of Cellular Automata

Michiel Rollier, Kallil M. C. Zielinski, Aisling J. Daly +2

Cellular automata (CAs) are fully-discrete dynamical models that have received much attention due to the fact that their relatively simple setup can nonetheless express highly comp…

cs.CV2023★ 2 cited

RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps

Leonardo Scabini, Kallil M. Zielinski, Lucas C. Ribas +3

Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature agg…

cs.SI2022★ 1 cited

A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata

Kallil M. C. Zielinski, Lucas C. Ribas, Jeaneth Machicao +1

Network modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transport, and many other real world complex systems. In additio…