Farzad Husain — accepted AI-conference papers

2017

Combining Semantic and Geometric Features for Object Class Segmentation of Indoor Scenes

Farzad Husain, Hannes Schulz, Babette Dellen, Carme Torras, Sven Behnke

RA-L 2017

Scene understanding is a necessary prerequisite for robots acting autonomously in complex environments. Low-cost RGB-D cameras such as Microsoft Kinect enabled new methods for analyzing indoor scenes and are now ubiquitously used in indoor robotics. We investigate strategies for efficient pixelwise

Cited by 54 Source ↗Scholar ↗

2016

Action Recognition Based on Efficient Deep Feature Learning in the Spatio-Temporal Domain

Farzad Husain, Babette Dellen, Carme Torras

RA-L 2016

Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an

Cited by 30 Source ↗Scholar ↗