In this paper, we propose a new variational formulation for simultaneous multiple motion segmentation and occlusion detection in an image sequence. For the representation of segmented regions, we use the multiphase level set method proposed by Vese and Chan. This method allows an efficient representation of up to 2^L regions with L level-set functions. Moreover, by construction, it enforces a domain partition with no gaps and overlaps. This is unlike previous variational approaches to multiple motion segmentation, where additional constraints were needed. The variational framework we propose can incorporate an arbitrary number of motion transformations as well as occlusion areas. In order to minimize the resulting energy, we developed a two-step algorithm. In the first step, we use a feature-based method to estimate the motions present in the image sequence. In the second step, based on the extracted motion information, we iteratively evolve all level set functions in the gradient descent direction to find the final segmentation. We have tested the above algorithm on both synthetic- and natural-motion data with very promising results. We show here segmentation results for two real video sequences.
In this paper, we propose a unified variational framework for tomographic reconstruction of 3-D dynamic objects. We use a geometric scene model, where the scene is assumed to be composed of discrete objects captured by their continuous surface boundaries. Object dynamics are modeled as consisting of separate intensity dynamics and object boundary dynamics. The shape dynamics are incorporated into our variational framework by defining a new distance measure between surfaces based on their signed distance functions, which is an extension of our previous definition of distance between curves. These models are then combined in a unified variational framework which incorporates the observation data, shape and intensity dynamics, and prior information on object spatial smoothness. The object surface and intensity sequences are estimated jointly as the minimizer of the resulting energy function. A coordinate descent algorithm based on surface evolution is developed to solve this nonlinear optimization problem. Efficient level set methods are used to implement the algorithm. This approach evolves the surfaces from their initial position to the final solution and handles topological uncertainties automatically.
Proc. SPIE. 5032, Medical Imaging 2003: Image Processing
KEYWORDS: Signal to noise ratio, Data modeling, 3D modeling, Tomography, Inverse problems, Distance measurement, Reconstruction algorithms, Single photon emission computed tomography, Autoregressive models, Affine motion model
In this paper, we propose a variational framework for tomographic
reconstruction of dynamic objects with unknown dynamic models. This is
an extension of our previous work on dynamic tomography using curve evolution methods where the shape dynamics are known a priori. We assume the dynamic model of the shape is a parameterized affine transform and propose a variational framework that incorporates information from observed data, intensity dynamics, spatial smoothness prior, and the dynamical shape model. A coordinate
descent algorithm based on a curve evolution method is then proposed for the joint estimation of the intensities, object boundary sequences, and the unknown dynamic model parameters. For implementation of the curve evolution and parameter estimation process, we use efficient level set methods.