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 4 has shown interesting results for semantic segmentation, but it is designed to segment images with nonoverlapped 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.

Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large margin. These architectures predict the directly observable semantic category of each pixel by usually optimizing a crossentropy loss. In this work we push the limit of semantic segmentation towards predicting semantic labels of directly visible as well as occluded objects or objects parts, where the networkxe2x80x99s input is a single depth image. We group the semantic categories into one background and multiple foreground object groups, and we propose a modification of the standard crossentropy loss to cope with the settings. In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task). The results are validated on a newly generated dataset (augmented from SUNCG) dataset.