Search Results for author:
Found 22 papers, 4 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)
Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks
The adjacency matrix, which provides a one-to-one representation of a complex network, can also yield several metrics of the graph.
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.
Improving Deep Neural Network Random Initialization Through Neuronal Rewiring
In this sense, PA rewiring only reorganizes connections, while preserving the magnitude and distribution of the weights.
Structure and Performance of Fully Connected Neural Networks: Emerging Complex Network Properties
Results show that these measures are highly related to the network classification performance.
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.
Multilayer Complex Network Descriptors for Color-Texture Characterization
A new method based on complex networks is proposed for color-texture analysis.
An optimized shape descriptor based on structural properties of networks
The proposed approach accounts for a more robust set of structural measurements, that improved the discriminant power of the shape descriptors.
Improving LBP and its variants using anisotropic diffusion
The main purpose of this paper is to propose a new preprocessing step in order to improve local feature descriptors and texture classification.
Clustering Algorithms: A Comparative Approach
We also found that the default configuration of the adopted implementations was not accurate.
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension
This work presents a novel descriptor for texture images based on fractal geometry and its application to image analysis.
Discrete Schroedinger Transform For Texture Recognition
This work presents a new procedure to extract features of grey-level texture images based on the discrete Schroedinger transform.
Authorship Attribution Based on Life-Like Network Automata
Remarkably, we have found a dependence of pre-processing steps (such as the lemmatization) on the obtained results, a feature that has mostly been disregarded in related works.
Texture analysis by multi-resolution fractal descriptors
The method is based on the Bouligand-Minkowski descriptors.
Texture analysis using volume-radius fractal dimension
The proposed approach expands the idea of the Mass-radius fractal dimension, a method originally developed for shape analysis, to a set of coordinates in 3D-space that represents the texture under analysis in a signature able to characterize efficiently different texture classes in terms of complexity.
Gabor wavelets combined with volumetric fractal dimension applied to texture analysis
Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition.
Performing edge detection by difference of Gaussians using q-Gaussian kernels
In image processing, edge detection is a valuable tool to perform the extraction of features from an image.
Multi-q Pattern Classification of Polarization Curves
An excellent classification rate was obtained, at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and both potential ranges, respectively, using only 2% of the original profile data.
