Deep learning methods for image segmentation containing translucent overlapped objects
Convolutional neural networks(CNN) are a subset of deep learning methods recently used widely for image segmentation. SegNet network  has shown interesting results for semantic segmentation, but it is designed to segment images with non-overlapped objects. However in some data translucent regions partially overlap. Having overlapped regions will cause methods not designed for overlapped objects to perform poorly or not work at all. To our knowledge no CNN has been designed yet to segment partially overlapped translucent objects.In this paper, we have designed a CNN to segment partially overlapped translucent regions. We used SegNet  as transfer learning for our overlapped image segmentation method. We also designed a new CNN with a simpler network for our data. Results on synthetic images give more than 95% segmentation accuracy for both methods.
|Deep learning, Image segmentation, Machine learning, Translucent overlapped images.|
|7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019|
|Organisation||Department of Systems and Computer Engineering|
Mahyari, T.L. (Tayebeh Lotfi), & Dansereau, R. (2019). Deep learning methods for image segmentation containing translucent overlapped objects. In GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings. doi:10.1109/GlobalSIP45357.2019.8969558