Image Understanding

Organizational Issues and Materials can be found at our Stud.IP page

The seminar will deal with the challenges of semantic perception in the context of robotics, presenting various aspects of it. Students will be presented with an overview of the field followed by individual presentations and reports of pre-defined topics.

Weakly supervised graph based semantic segmentation by learning communities of image-parts http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf

Decision Making under Uncertain Segmentations http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7139359

KAZE Features + Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla12eccv.pdf http://www.robesafe.com/personal/pablo.alcantarilla/papers/Alcantarilla13bmvc.pdf

B-SHOT: A Binary Feature Descriptor for Fast and Efficient Keypoint Matching on 3D Point Clouds http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7353630

Rotation and Translation Invariant 3D Descriptor for Surfaces http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7353450

Real-time Pose Detection and Tracking of Hundreds of Objects + SimTrack: A Simulation-based Framework for Scalable Real-time Object Pose Detection and Tracking http://www.karlpauwels.com/downloads/tcsvt_2015/Pauwels_IEEE_TCSVT_2015.pdf http://www.karlpauwels.com/downloads/iros_2015/Pauwels_IROS_2015.pdf

Surface Oriented Traverse for Robust Instance Detection in RGB-D http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7353983

RGB-D Object Modelling for Object Recognition and Tracking http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7353360

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks http://arxiv.org/abs/1506.01497

Rich feature hierarchies for accurate object detection and semantic segmentation http://arxiv.org/abs/1311.2524

Efficient RGB-D Object Categorization Using cascaded Ensembles of Randomized Decision Trees http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7139358

Robust 3D tracking of Unknown Objects http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7139520

Depth-Based Tracking with Physical Constraints for Robot Manipulation http://homes.cs.washington.edu/~tws10/DepthBasedTracking.pdf

AfNet: The Affordance Network http://link.springer.com/chapter/10.1007%2F978-3-642-37331-2_39

Affordance detection of Tool parts from Geometric Features http://www.visionmeetscognition.org/fpic2014/Camera_Ready/Paper%2035.pdf

Long-term human affordance maps http://dx.doi.org/10.1109/IROS.2015.7354193

Visualizing and Understanding Convolutional Networks https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf

DeepFace: Closing the Gap to Human-Level Performance in Face Verification https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf

MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation http://cs.nyu.edu/~ajain/accv2014/paper.pdf

Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation https://www.robots.ox.ac.uk/~vgg/rg/papers/tompson2014.pdf

Flowing ConvNets for Human Pose Estimation in Videos https://www.robots.ox.ac.uk/~vgg/publications/2015/Pfister15a/pfister15a.pdf

Multimodal deep learning for robust RGB-D object recognition http://arxiv.org/pdf/1507.06821v2.pdf

RGB-D Object Recognition and Pose Estimation Based on Pre-Trained Convolutional Neural Network Features https://www.ais.uni-bonn.de/papers/ICRA_2015_Schwarz_RGB-D-Objects_Transfer-Learning.pdf

Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition http://yann.lecun.com/exdb/publis/pdf/ranzato-cvpr-07.pdf

Convolution Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations http://web.eecs.umich.edu/~honglak/icml09-ConvolutionalDeepBeliefNetworks.pdf

Sparse Feature Learning for Deep Belief Networks https://papers.nips.cc/paper/3363-sparse-feature-learning-for-deep-belief-networks.pdf

Efficient sparse coding algorithms https://papers.nips.cc/paper/2979-efficient-sparse-coding-algorithms.pdf

Automatic initialization for skeleton tracking in optical motion capture http://dx.doi.org/10.1109/ICRA.2015.7139260

Unsupervised robot learning to predict person motion http://dx.doi.org/10.1109/ICRA.2015.7139254

Pose estimation for a partially observable human body from RGB-D cameras http://dx.doi.org/10.1109/IROS.2015.7354068

Real-time full-body human attribute classification in RGB-D using a tessellation boosting approach http://dx.doi.org/10.1109/IROS.2015.7353541

Learning symbolic representations of actions from human demonstrations http://dx.doi.org/10.1109/ICRA.2015.7139728

Fast Target Prediction of Human Reaching Motion for Cooperative Human-Robot Manipulation Tasks Using Time Series Classification http://dx.doi.org/10.1109/ICRA.2015.7140066

Effective 3D action recognition using EigenJoints http://dx.doi.org/10.1016/j.jvcir.2013.03.001

Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition http://dx.doi.org/10.1016/j.jvcir.2013.04.007

Unsupervised Temporal Segmentation of Repetitive Human Actions Based on Kinematic Modeling and Frequency Analysis http://arxiv.org/abs/1512.04115

sEMG-based decoding of detailed human intentions from finger-level hand motions http://dx.doi.org/10.1109/IROS.2015.7353982

Human motion classification and recognition using wholebody contact force http://dx.doi.org/10.1109/IROS.2015.7353979

Context-based intent understanding using an Activation Spreading architecture http://dx.doi.org/10.1109/IROS.2015.7353791

A framework for unsupervised online human reaching motion recognition and early prediction http://dx.doi.org/10.1109/IROS.2015.7353706

Human intention inference and motion modeling using approximate E-M with online learning http://dx.doi.org/10.1109/IROS.2015.7353614

These four papers count as one block, i.e. they have to be presented together.

RoboSherlock: Unstructured Information Processing for Robot Perception http://ai.uni-bremen.de/_media/paper/beetz15robosherlock.pdf

RoboSherlock: Unstructured Information Processing Framework for Robotic Perception http://dx.doi.org/10.1007/978-3-319-26327-4_8

Pervasive 'Calm' Perception for Autonomous Robotic Agents http://ai.uni-bremen.de/_media/paper/Wiedemeyer15pervasive.pdf

Perception for Everyday Human Robot Interaction http://dx.doi.org/10.1007/s13218-015-0400-1