2,284 research outputs found
Enhancing motion trajectory segmentation of rigid bodies using a novel screw-based trajectory-shape representation
Trajectory segmentation refers to dividing a trajectory into meaningful
consecutive sub-trajectories. This paper focuses on trajectory segmentation for
3D rigid-body motions. Most segmentation approaches in the literature represent
the body's trajectory as a point trajectory, considering only its translation
and neglecting its rotation. We propose a novel trajectory representation for
rigid-body motions that incorporates both translation and rotation, and
additionally exhibits several invariant properties. This representation
consists of a geometric progress rate and a third-order trajectory-shape
descriptor. Concepts from screw theory were used to make this representation
time-invariant and also invariant to the choice of body reference point. This
new representation is validated for a self-supervised segmentation approach,
both in simulation and using real recordings of human-demonstrated pouring
motions. The results show a more robust detection of consecutive submotions
with distinct features and a more consistent segmentation compared to
conventional representations. We believe that other existing segmentation
methods may benefit from using this trajectory representation to improve their
invariance.Comment: This work has been submitted to the IEEE International Conference on
Robotics and Automation (ICRA) for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
Temporal accumulation of oriented visual features
In this paper we present a framework for accumulating on-line a model of a moving object (e.g., when manipulated by a robot). The proposed scheme is based on Bayesian filtering of local features, filtering jointly position, orientation and appearance information. The work presented here is novel in two aspects: first, we use an estimation mechanism that updates iteratively not only geometrical information, but also appearance information. Second, we propose a probabilistic version of the classical n-scan criterion that allows us to select which features are preserved and which are discarded, while making use of the available uncertainty model.
The accumulated representations have been used in three different contexts: pose estimation, robotic grasping, and driver assistance scenario
- …