We present a new integrated approach to the two-dimensional part segmentation, shape, and motion estimation of moving multipart objects. Our technique exploits the relationship between the geometry and the observed deformations of the apparent contour of a moving multipart object and its structure. The novelty of the technique is that no prior model of the object or of its parts is employed. We develop a Part Segmentation Algorithm (PSA) that recursively recovers all the moving parts of an object by monitoring and reasoning over the changes of its deforming apparent contour. To parameterize and segment over time a deforming apparent contour, we fit initially a single deformable model whose global and local deformations over time allow us to hypothesize an underlying part structure. This hypothesis is verified by further monitoring the relative motion among the model's parts and the satisfaction of certain criteria. Upon verifying the part hypothesis, the initial deformable model is split into two or more models that better fit the apparent contour. This recursive operation allows the refinement over time of the number and shape of the extracted parts. When multiple deformable models are used to model the apparent contour of a multipart object, there is an uncertainty concerning the deformable model to which the data points should apply forces to. To address this problem, we present a new algorithm for force assignment that assigns forces from the data to multiple models. This algorithm allows partial overlap between the parts' models and the determination of their joint location. Finally, the effectiveness of the approach is demonstrated through a series of experiments involving a variety of objects.
|Original language||English (US)|
|Number of pages||19|
|Journal||Computer Vision and Image Understanding|
|State||Published - Feb 1997|
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition