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Communication Dans Un Congrès Année : 2020

Semantic Segmentation Post-processing with Colorized Pairwise Potentials and Deep Edges

Résumé

Semantic segmentation is the task of assigning a label to each pixel in an image, providing high level insights to a wide range of end-user applications like autonomous driving, medical imaging and land use mapping. However, semantic segmentation results are not always consistent with the object boundaries and may sometimes lack of spatial consistency. To solve these problems, post-processing algorithms have been proposed, paving the way for more robust pipelines. In this work, we study a novel post-processing approach to enhance semantic segmentation of panchromatic aerial images based on unsupervised colorization and deep edge superpixels. In particular, we propose to assess whether applying a colorization algorithm could enhance the strength of the pairwise potentials used in an extended dense conditional random field. We present experiments on recent aerial color images that we convert to grayscale before colorization, allowing us to assess how colorized representations impact post-processing when compared to real color and panchromatic representations.
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Dates et versions

hal-03067571 , version 1 (15-12-2020)

Identifiants

Citer

Rémi Ratajczak, Carlos F Crispim-Junior, Béatrice Fervers, Élodie Faure, Laure Tougne. Semantic Segmentation Post-processing with Colorized Pairwise Potentials and Deep Edges. International Conference on Image Processing Theory, Tools and Applications (IPTA 2020), IEEE, Nov 2020, Paris, France. ⟨10.1109/IPTA50016.2020.9286622⟩. ⟨hal-03067571⟩
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