no code implementations • 12 Apr 2024 • Kallil M. Zielinski, Leonardo Scabini, Lucas C. Ribas, Núbia R. da Silva, Hans Beeckman, Jan Verwaeren, Odemir M. Bruno, Bernard De Baets
In recent years, we have seen many advancements in wood species identification.
1 code implementation • 8 Mar 2023 • Leonardo Scabini, Kallil M. Zielinski, Lucas C. Ribas, Wesley N. Gonçalves, Bernard De Baets, Odemir M. Bruno
Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied.
Ranked #1 on Image Classification on KTH-TIPS2 (using extra training data)
no code implementations • 18 Nov 2022 • Kallil M. C. Zielinski, Lucas C. Ribas, Jeaneth Machicao, Odemir M. Bruno
Network modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transport, and many other real world complex systems.
no code implementations • 10 Jul 2020 • Lucas C. Ribas, Leonardo F. S. Scabini, Jarbas Joaci de Mesquita Sá Junior, Odemir M. Bruno
Experimental results show a high classification performance of the proposed method when compared to other methods, indicating that our approach can be used in many image analysis problems.
1 code implementation • 13 Sep 2019 • Leonardo F. S. Scabini, Lucas C. Ribas, Odemir M. Bruno
Texture is one of the most-studied visual attribute for image characterization since the 1960s.
no code implementations • 27 Jun 2018 • Lucas C. Ribas, Wesley N. Goncalves, Odemir M. Bruno
In this paper, a new method for dynamic texture characterization based on diffusion in directed networks is proposed.
no code implementations • 24 Jun 2018 • Lucas C. Ribas, Jarbas J. M. Sa Junior, Leonardo F. S. Scabini, Odemir M. Bruno
This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks.