MaReconstrSeg

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Deep Learning for Semantic Segmentation of Human Bodies

We are looking for a motivated student who wants to inspect a deep learning approach, in tackling the challenging problem of semantic segmentation of human bodies. This task will be one of the core modules of a 3D reconstruction framework we are currently developing, in collaboration with a company that works on skin lesion detection. Given a set of depth maps of the target object from multiple views, the goal is to develop a method that identifies the body parts in each of them. Specifically, we intend to explore the potential of Convolutional Neural Networks (CNNs) in this scenario. Previous work has been done in using CNNs to infer semantic segmentation from RGB data. In absence of color information, the semantic segmentation becomes more challenging. With this in mind, we utilize the approach in [1], where a dense correspondence is found between two depth images of humans. In our task, having a known segmentation map for a reference depth image and assigning such correspondence, it is possible to infer the segmentation for new target depth maps.

[1] Lingyu Wei, Qixing Huang, Duygu Ceylan, Etienne Vouga, Hao Li. Dense Human Body Correspondences Using Convolutional Networks. CVPR. 2016.



Supervision: Helisa Dhamo, Dr. Federico Tombari


For further information please contact: Helisa Dhamo



ProjectForm
Title: Deep Learning for Semantic Segmentation of Human Bodies
Abstract: In this project we want to inspect a deep learning approach, to tackle the challenging problem of semantic segmentation of human bodies. This task will be one of the core modules of a 3D reconstruction framework we are currently developing. Given a set of depth maps of the target object from multiple views, the goal is to develop a method that identifies the body parts in each of them. Specifically, we intend to explore the potential of Convolutional Neural Networks (CNNs) in this scenario. Previous work has been done in using CNNs to infer semantic segmentation from RGB data. In absence of color information, the semantic segmentation becomes more challenging. With this in mind, we utilize the approach in [1], where a dense correspondence is found between two depth images of humans. In our task, having a known segmentation map for a reference depth image and assigning such correspondence, it is possible to infer the segmentation for new target depth maps. [1] Lingyu Wei, Qixing Huang, Duygu Ceylan, Etienne Vouga, Hao Li. Dense Human Body Correspondences Using Convolutional Networks. CVPR. 2016.
Student:  
Director: Prof. Nassir Navab
Supervisor: Helisa Dhamo, Federico Tombari
Type: Master Thesis
Area: Segmentation, Machine Learning, Computer Vision
Status: open
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.4 - 20 Sep 2017 - 13:35 - HelisaDhamo