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A Modular Approach for Object Segmentation and Image Inpainting
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| 66.67 MB | Adobe PDF |
Autores
Resumo(s)
In this project, we propose ModInPainTor, a modular solution for object segmentation
and removal in images. ModInPainTor takes advantage of advanced models from literature
to deliver high-quality image inpainting results. This solution is inspired by
InPainTor, a previous project that utilized a highly-integrated Convolutional Neural
Network-based model for object detection and removal but lacked a comprehensive
evaluation of its performance. We conduct a thorough evaluation of the InPainTor and
ModInPainTor solutions, assessing their effectiveness in anonymizing visual data while
maintaining data quality. The evaluation involves quantitative metrics to measure the
quality of the results and a qualitative analysis to evaluate the visual quality of the
anonymized images. Furthermore, we investigate the impact of object segmentation
accuracy on the quality of the anonymized images. Our findings indicate that Mod-
InPainTor significantly improves the visual quality of anonymized images compared
to InPainTor, albeit with increased computational requirements. We find that the proposed
solution could be effectively employed in industrial settings where anonymization
quality is prioritized over computational efficiency.
Descrição
Palavras-chave
Anonymization Image inpainting Object segmentation Object removal