<p>This thesis presents work done within the EC-founded project VISATEC. Due to the different directions of the VISATEC project this thesis has a few different threads.</p><p>A novel presentation scheme for medium level vision features applied to range sensor data and to image sequences. Some estimation procedures for this representation have been implemented and tested. The representation is tensor based and uses higher order tensors in a projective space. The tensor can hold information on several local structures including their relative position and orientation. This information can also be extracted from the tensor.</p><p>A number of well-known techniques are combined in a novel way to be able to perform object pose estimation under changes of the object in position, scale and rotation from a single 2D image. The local feature used is a patch which is resampled in a log-polar pattern. A number of local features are matched to a database and the k nearest neighbors vote an object state parameters. This most probable object states are found through mean-shift clustering.</p><p>A system using multi-cue integration as a means of reaching a higher level of system-level robustness and a higher lever of accuracy is developed and evaluated in an industrial-like-setting. The system is based around a robotic manipulator arm with an attached camera. The system is designed to solve parts of the bin-picking problem. The above mentioned 2D technique for object pose estimation is also evaluated within this system.</p>

This thesis presents work done within the EC-founded project VISATEC. Due to the different directions of the VISATEC project this thesis has a few different threads. A novel presentation scheme for medium level vision features applied to range sensor data and to image sequences. Some estimation procedures for this representation have been implemented and tested. The representation is tensor based and uses higher order tensors in a projective space. The tensor can hold information on several local structures including their relative position and orientation. This information can also be extracted from the tensor. A number of well-known techniques are combined in a novel way to be able to perform object pose estimation under changes of the object in position, scale and rotation from a single 2D image. The local feature used is a patch which is resampled in a log-polar pattern. A number of local features are matched to a database and the k nearest neighbors vote an object state parameters. This most probable object states are found through mean-shift clustering. A system using multi-cue integration as a means of reaching a higher level of system-level robustness and a higher lever of accuracy is developed and evaluated in an industrial-like-setting. The system is based around a robotic manipulator arm with an attached camera. The system is designed to solve parts of the bin-picking problem. The above mentioned 2D technique for object pose estimation is also evaluated within this system.