446 citations · 2.3k across the 135 of their papers we have counts for
11 papers · 2 filters
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…
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…
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…
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…