925 citations · 1.2k across the 15 of their papers we have counts for
17 papers
HarmoniDiff-RS: Training-Free Diffusion Harmonization for Satellite Image Composition
Xiaoqi Zhuang, Jefersson A. Dos Santos, Jungong Han
Satellite image composition plays a critical role in remote sensing applications such as data augmentation, disaste simulation, and urban planning. We propose HarmoniDiff-RS, a tra…
FLIM Networks with Bag of Feature Points
João Deltregia Martinelli, Marcelo Luis Rodrigues Filho, Felipe Crispim da Rocha Salvagnini +3
Convolutional networks require extensive image annotation, which can be costly and time-consuming. Feature Learning from Image Markers (FLIM) tackles this challenge by estimating e…
Minimizing Risk Through Minimizing Model-Data Interaction: A Protocol For Relying on Proxy Tasks When Designing Child Sexual Abuse Imagery Detection Models
Thamiris Coelho, Leo S. F. Ribeiro, João Macedo +2
The distribution of child sexual abuse imagery (CSAI) is an ever-growing concern of our modern world; children who suffered from this heinous crime are revictimized, and the growin…
Neglected Risks: The Disturbing Reality of Children's Images in Datasets and the Urgent Call for Accountability
Carlos Caetano, Gabriel O. dos Santos, Caio Petrucci +6
Including children's images in datasets has raised ethical concerns, particularly regarding privacy, consent, data protection, and accountability. These datasets, often built by sc…
Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery
Pedro H. V. Valois, João Macedo, Leo S. F. Ribeiro +2
Crime in the 21st century is split into a virtual and real world. However, the former has become a global menace to people's well-being and security in the latter. The challenges i…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
YOLOv7 for Mosquito Breeding Grounds Detection and Tracking
Camila Laranjeira, Daniel Andrade, Jefersson A. dos Santos
With the looming threat of climate change, neglected tropical diseases such as dengue, zika, and chikungunya have the potential to become an even greater global concern. Remote sen…
FuSS: Fusing Superpixels for Improved Segmentation Consistency
Ian Nunes, Matheus B. Pereira, Hugo Oliveira +2
In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the…
Facing the Void: Overcoming Missing Data in Multi-View Imagery
Gabriel Machado, Keiller Nogueira, Matheus Barros Pereira +1
In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from…
ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation
Matheus Barros Pereira, Jefersson Alex dos Santos
Labeling semantic segmentation datasets is a costly and laborious process if compared with tasks like image classification and object detection. This is especially true for remote…
An Introduction to Deep Morphological Networks
Keiller Nogueira, Jocelyn Chanussot, Mauro Dalla Mura +1
The recent impressive results of deep learning-based methods on computer vision applications brought fresh air to the research and industrial community. This success is mainly due…
Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
Keiller Nogueira, Jefersson A. dos Santos, Nathalia Menini +3
Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plan…
Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot +2
Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Towards such goal, Convolutional Networks can learn specific…
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization
Érico M. Pereira, Ricardo da S. Torres, Jefersson A. dos Santos
Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature learning approaches have…
Exploiting ConvNet Diversity for Flooding Identification
Keiller Nogueira, Samuel G. Fadel, Ícaro C. Dourado +7
Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure towards flood monitoring is bas…
Meat adulteration detection through digital image analysis of histological cuts using LBP
João J. de Macedo Neto, Jefersson A. dos Santos, William Robson Schwartz
Food fraud has been an area of great concern due to its risk to public health, reduction of food quality or nutritional value and for its economic consequences. For this reason, it…
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
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 tra…