Deep Learning Methods for Image Segmentation Containing Translucent Overlapped Objects

Abstract

Convolutional neural networks(CNN) are a subset of deep learning methods recently used widely for image segmentation. SegNet network [4] 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 [4] 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.