Search Results for author: Keiller Nogueira

Found 12 papers, 6 papers with code

GMM-IL: Image Classification using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes

no code implementations1 Dec 2022 Penny Johnston, Keiller Nogueira, Kevin Swingler

Current deep learning classifiers, carry out supervised learning and store class discriminatory information in a set of shared network weights.

Image Classification

AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification

no code implementations3 Aug 2020 Gabriel Machado, Edemir Ferreira, Keiller Nogueira, Hugo Oliveira, Pedro Gama, Jefersson A. dos Santos

Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery.

General Classification Image Classification +1

Fully Convolutional Open Set Segmentation

1 code implementation25 Jun 2020 Hugo Oliveira, Caio Silva, Gabriel L. S. Machado, Keiller Nogueira, Jefersson A. dos Santos

In semantic segmentation knowing about all existing classes is essential to yield effective results with the majority of existing approaches.

Open Set Learning Segmentation +1

An Introduction to Deep Morphological Networks

no code implementations4 Jun 2019 Keiller Nogueira, Jocelyn Chanussot, Mauro Dalla Mura, Jefersson A. dos Santos

Results show that the proposed DeepMorphNets is a promising technique that can learn distinct features when compared to the ones learned by current deep learning methods.

Image Classification

Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks

1 code implementation11 Apr 2018 Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William R. Schwartz, Jefersson A. dos Santos

A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties.

Semantic Segmentation

Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification

1 code implementation4 Feb 2016 Keiller Nogueira, Otávio A. B. Penatti, Jefersson A. dos Santos

We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors.

General Classification Scene Classification

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