80 citations · 143 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
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)…
Evaluation of Activated Sludge Settling Characteristics from Microscopy Images with Deep Convolutional Neural Networks and Transfer Learning
Sina Borzooei, Leonardo Scabini, Gisele Miranda +7
Microbial communities play a key role in biological wastewater treatment processes. Activated sludge settling characteristics, for example, are affected by microbial community comp…
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…
Learning Local Complex Features using Randomized Neural Networks for Texture Analysis
Lucas C. Ribas, Leonardo F. S. Scabini, Jarbas Joaci de Mesquita Sá Junior +1
Texture is a visual attribute largely used in many problems of image analysis. Currently, many methods that use learning techniques have been proposed for texture discrimination, a…
Spatio-spectral networks for color-texture analysis
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. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale…
Fusion of complex networks and randomized neural networks for texture analysis
Lucas C. Ribas, Jarbas J. M. Sa Junior, Leonardo F. S. Scabini +1
This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is model…
Multilayer Complex Network Descriptors for Color-Texture Characterization
Leonardo F S Scabini, Rayner H M Condori, Wesley N Gonçalves +1
A new method based on complex networks is proposed for color-texture analysis. The proposal consists on modeling the image as a multilayer complex network where each color channel…