On July, 14th of 2021, an article authored by Pedro Messias, Maria João Sousa and Alexandra Moutinho [1], was presented at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), a flagship annual international conference of the IEEE Computational Intelligence Society focused on fuzzy systems.
The paper presents a fire data annotation method that leverages semantic segmentation based on computer vision and optimization techniques, namely through superpixel aggregation and color features.
The approach introduces interpretable linguistic models that generate pixel-wise fire segmentation and annotations, which are explainable through simple fine-tunable rules that can support subsequent annotation validation by fire domain experts.
The article is available online ( https://ieeexplore.ieee.org/document/9494421/ ), and also available on this page.
Link to the file on ResearchGate:
https://www.researchgate.net/publication/353723135_Color-based_Superpixel_Semantic_Segmentation_for_Fire_Data_Annotation3.2021.9494421.
Referência:
[1] P. Messias, M. J. Sousa, and A. Moutinho. (2021). “Color-based Superpixel Semantic Segmentation for Fire Data Annotation” in IEEE International Conference on Fuzzy Systems (FUZZ–IEEE), Luxembourg, Luxembourg, 2021, pp.1–7. IEEE. doi: 10.1109/FUZZ45933.2021.9494421.
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