Publications

PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects

Published in European Conference of Computer Vision Workshop, 2024

Plastic waste in aquatic environments poses severe risks to marine life and human health. Autonomous robots can be utilized to collect floating waste, but they require accurate object identification capability. While deep learning has been widely used as a powerful tool for this task, its performance is significantly limited by outdoor light conditions and water surface reflection. Light polarization, abundant in such environments yet invisible to the human eye, can be captured by modern sensors to significantly improve litter detection accuracy on water surfaces. With this goal in mind, we introduce PoTATO, a dataset containing 12,380 labeled plastic bottles and rich polarimetric information. We demonstrate under which conditions polarization can enhance object detection and, by providing raw image data, we offer an opportunity for the research community to explore novel approaches and push the boundaries of state-of-the-art object detection algorithms even further. Code and data are publicly available at https://github.com/luisfelipewb/PoTATO/tree/eccv2024 .

Recommended citation: Luis Felipe Wolf Batista, Salim Khazem, Mehran Adibi, Seth Hutchinson, Cedric Pradalier. PoTATO: A Dataset for Analyzing Polarimetric Traces of Afloat Trash Objects. arXiv preprint arXiv:2308.11291, 2023. https://arxiv.org/pdf/2409.12659

The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark

Published in Journal of Neural Engineering, 2024

Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and establish open and reproducible benchmarks for effective comparison within the field. The need for such benchmark lies in the rapid industrial progress that has given rise to undisclosed proprietary solutions. Furthermore, the scientific literature is dense, often featuring challenging-to-reproduce evaluations, making comparisons between existing approaches arduous. Approach. Within an open framework, 30 machine learning pipelines (separated into raw signal: 11, Riemannian: 13, deep learning: 6) are meticulously re-implemented and evaluated across 36 publicly available datasets, including motor imagery (14), P300 (15), and SSVEP (7). The analysis incorporates statistical meta-analysis techniques for results assessment, encompassing execution time and environmental impact considerations. Main results. The study yields principled and robust results applicable to various BCI paradigms, emphasizing motor imagery, P300, and SSVEP. Notably, Riemannian approaches utilizing spatial covariance matrices exhibit superior performance, underscoring the necessity for significant data volumes to achieve competitive outcomes with deep learning techniques. The comprehensive results are openly accessible, paving the way for future research to further enhance reproducibility in the BCI domain. Significance. The significance of this study lies in its contribution to establishing a rigorous and transparent benchmark for BCI research, offering insights into optimal methodologies and highlighting the importance of reproducibility in driving advancements within the field.

Recommended citation: CHEVALLIER, Sylvain, CARRARA, Igor, ARISTIMUNHA, Bruno, et al. The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark. arXiv preprint arXiv:2404.15319, 2024. https://arxiv.org/pdf/2404.15319

Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation

Published in International Conference on Computer Vision Systems, 2023

The quality of a wood log in the wood industry depends heavily on the presence of both outer and inner defects, including inner knots that are a result of the growth of tree branches. Today, locating the inner knots require the use of expensive equipment such as X-ray scanners. In this paper, we address the task of predicting the location of inner defects from the outer shape of the logs. The dataset is built by extracting both the contours and the knots with X-ray measurements. We propose to solve this binary segmentation task by leveraging convolutional recurrent neural networks. Once the neural network is trained, inference can be performed from the outer shape measured with cheap devices such as laser profilers. We demonstrate the effectiveness of our approach on fir and spruce tree species and perform ablation on the recurrence to demonstrate its importance

Recommended citation: KHAZEM, Salim, FIX, Jeremy, et PRADALIER, Cedric. Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation. arXiv preprint arXiv:2308.11291, 2023. https://arxiv.org/pdf/2308.11291.pdf

Deep learning for the detection of semantic features in tree X-ray CT scans

Published in Journal: Artificial Intelligence in Agriculture, 2023

According to the industry, the value of wood logs is heavily influenced by their internal structure, particularly the distribution of knots within the trees. Nowadays, CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees. Knowing where the tree semantic features, especially knots, contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products. However, this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline, knot localization and log contour, in a robust and efficient manner. In this paper, we propose an effective methodology based on deep learning for performing these different tasks by processing CT-scanner images with deep convolutional neural networks. To meet this objective, three end-to-end trainable pipelines are proposed. The first pipeline is focused on centers detection using CNNs architecture with a regression head, the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture. The different architectures are tested on several tree species. With these experiments, we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness. The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

Recommended citation: KHAZEM, Salim, RICHARD, Antoine, FIX, Jeremy, et al. Deep learning for the detection of semantic features in tree X-ray CT scans. Artificial Intelligence in Agriculture, 2023, vol. 7, p. 13-26, doi: 10.1016/j.aiia.2022.12.001 https://www.sciencedirect.com/science/article/pii/S2589721722000289

Minimizing subject-dependent calibration for BCI with Riemannian transfer learning

Published in 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021

Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the cognitive fatigue, before even starting to use the BCI. Reducing or suppressing this subject-dependent calibration is possible by relying on advanced machine learning techniques, such as transfer learning. Building on Riemannian BCI, we present a simple and effective scheme to train a classifier on data recorded from different subjects, to reduce the calibration while preserving good performances. The main novelty of this paper is to propose a unique approach that could be applied on very different paradigms. To demonstrate the robustness of this approach, we conducted a meta-analysis on multiple datasets for three BCI paradigms: event-related potentials (P300), motor imagery and SSVEP. Relying on the MOABB open source framework to ensure the reproducibility of the experiments and the statistical analysis, the results clearly show that the proposed approach could be applied on any kind of BCI paradigm and in most of the cases to significantly improve the classifier reliability. We point out some key features to further improve transfer learning methods.

Recommended citation: S. Khazem, S. Chevallier, Q. Barthélemy, K. Haroun and C. Noûs, "Minimizing Subject-dependent Calibration for BCI with Riemannian Transfer Learning," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021, pp. 523-526, doi: 10.1109/NER49283.2021.9441279 https://arxiv.org/pdf/2111.12071.pdf