Giacomo Bastiani
Enhanced Normalized Cuts with Spectral Weight Adjustment for Image Segmentation.
Rel. Edoardo Fadda. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2024
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Abstract: |
This work investigates an enhancement to the normalized cuts algorithm, introducing a preliminary spectral segmentation analysis to make the process of binary image segmentation more effective. In particular, we propose two improvements: (i) use the results of the proposed preliminary spectral clustering algorithm as a prior for the final segmentation (ii) use Bayesian optimization coupled with Gaussian processes to tunes the hyperparameters. Then, using the results from this procedure, and by maximizing a confidence measure it is possible to obtain a set of pixels belonging to the foreground and the background. These pixels are passed to a min-cut/max-flow algorithm as the source and sink nodes for further refinement, therefore automating the process of foreground/background pixel selection for this algorithm. Finally, the normalized cuts algorithm has been customized to perform video segmentation in real time. Experiments on a subset of the MSRA10K dataset and in different video conditions showed relevant improvements in segmentation quality and real-time usage. The proposed methodologies have the potential to be the starting point for more advanced techniques in fields requiring precise and efficient video segmentation, such as hand tracking. |
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Relators: | Edoardo Fadda |
Academic year: | 2023/24 |
Publication type: | Electronic |
Number of Pages: | 67 |
Subjects: | |
Corso di laurea: | Corso di laurea magistrale in Ingegneria Matematica |
Classe di laurea: | New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING |
Aziende collaboratrici: | UNSPECIFIED |
URI: | http://webthesis.biblio.polito.it/id/eprint/31612 |
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