Embodied Synaptic Plasticity With Online Reinforcement Learning.

The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop.

Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms.

Any visual sensor, whether artificial or biological, maps the 3D-world on a 2D-representation. The missing dimension is depth and most species use stereo vision to recover it. Stereo vision implies multiple perspectives and matching, hence it obtains depth from a pair of images.

Projective biomechanical depth matching for soft tissue registration in laparoscopic surgery.

Purpose: A key component of computer- assisted surgery systems is the accurate and robust registration of preoperative planning data with intraoperative sensor data. In laparoscopic surgery, this image-based registration remains challenging due to soft tissue deformations. This paper presents a novel approach for biomechanical soft tissue registration of preoperative CT data with stereo endoscopic image data.

Intraoperative on-the-fly organ-mosaicking for laparoscopic surgery.

The goal of computer-assisted surgery is to provide the surgeon with guidance during an intervention, e.g., using augmented reality.

Physics-based shape matching for intraoperative image guidance.

Purpose: Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model.