446 citations · 2.3k across the 135 of their papers we have counts for
14 papers · 1 filter
Cross-Modal Manifold Learning for Cross-modal Retrieval
Sailesh Conjeti, Anees Kazi, Nassir Navab +1
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space. Unlike existing approaches that compromise between preservi…
Deep Residual Hashing
Sailesh Conjeti, Abhijit Guha Roy, Amin Katouzian +1
Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks. Most existing hashing methods first encode t…
Exploring Non-Reversing Magic Mirrors for Screen-Based Augmented Reality Systems
Felix Bork, Roghayeh Barmaki, Ulrich Eck +3
Screen-based Augmented Reality (AR) systems can be built as a window into the real world as often done in mobile AR applications or using the Magic Mirror metaphor, where users can…
X-ray In-Depth Decomposition: Revealing The Latent Structures
Shadi Albarqouni, Javad Fotouhi, Nassir Navab
X-ray radiography is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics…
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Sebastian Pölsterl, Nassir Navab, Amin Katouzian
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large a…
The TUM LapChole dataset for the M2CAI 2016 workflow challenge
Ralf Stauder, Daniel Ostler, Michael Kranzfelder +3
In this technical report we present our collected dataset of laparoscopic cholecystectomies (LapChole). Laparoscopic videos of a total of 20 surgeries were recorded and annotated w…
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
Gerda Bortsova, Michael Sterr, Lichao Wang +6
Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understo…
An Octree-Based Approach towards Efficient Variational Range Data Fusion
Wadim Kehl, Tobias Holl, Federico Tombari +2
Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime. Especially for sparse geometric structures, volumetric representations produce a h…
Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study
Christoph Hennersperger, Bernhard Fuerst, Salvatore Virga +4
Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided u…
Hashmod: A Hashing Method for Scalable 3D Object Detection
Wadim Kehl, Federico Tombari, Nassir Navab +2
We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashi…
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
Wadim Kehl, Fausto Milletari, Federico Tombari +2
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting. For regression, we employ a convolutional auto-encoder…
Deep Active Contours
Christian Rupprecht, Elizabeth Huaroc, Maximilian Baust +1
We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional…
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Felix Grün, Christian Rupprecht, Nassir Navab +1
Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance. However, understanding what a network has learned still proves to be a challenging…
Deeper Depth Prediction with Fully Convolutional Residual Networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis +2
This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to…