Search Results for author:
Found 7 papers, 2 papers with code
Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion
In recent years, we have seen many advancements in wood species identification.
RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
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 DTD
(using extra training data)
A Network Classification Method based on Density Time Evolution Patterns Extracted from Network Automata
Network modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transport, and many other real world complex systems.
Learning Local Complex Features using Randomized Neural Networks for Texture Analysis
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.
Spatio-spectral networks for color-texture analysis
Texture is one of the most-studied visual attribute for image characterization since the 1960s.
Dynamic texture analysis with diffusion in networks
In this paper, a new method for dynamic texture characterization based on diffusion in directed networks is proposed.
Fusion of complex networks and randomized neural networks for texture analysis
This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks.
