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Published in:
2022 | OriginalPaper | Chapter
Case Study: Instance Segmentation
Authors : Liang Wang, Jianxin Zhao, Richard Mortier
Published in: OCaml Scientific Computing
Publisher: Springer International Publishing
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Computer vision is a field that automates tasks such as ascribing highlevel descriptions to images and videos. It has been applied to a wide variety of domains ranging from the highly technical such as automatic tagging of satellite images or analysis of medical imaging [1] [2], to the more mundane such as categorising pictures in your phone or creating an emoji from a picture of your face. This field has seen tremendous progress since 2012, when A. Krizhevsky et al. first used deep learning in computer vision and crushed their opponents in the ImageNet challenge [3]. We have already discussed the image recognition task in the previous chapter. Here we introduce another classical computer vision task, Instance Segmentation, which labels objects within an image. We will discuss its connection with other similar applications, how the deep neural networks are constructed in OCaml, and how such a network when loaded with pre-trained weights, can be used to process users’ input images.