In this paper, we present a new approach for segmenting regions of bone in MRI volumes using a deformable model. Our method takes into account the partial volume effects that occur with MRI data, thus permitting a precise segmentation of these bone regions. Partial volume is estimated, in a narrow band around the deformable model, at each iteration of the propagation of the model. Segmentation of the skull in medical imagery is an important stage in applications that require the construction of realistic models of the head. Such models are used, for example, to simulate the behavior of electro-magnetic fields in the head and to model the electrical activity of the cortex in EEG and MEG data.
Numerical computation with Bayesian posterior densities has recently received much attention both in the statistics and computer vision communities. This paper explores the computation of marginal distributions for models that have been widely considered in computer vision. These computations can be used to assess homogeneity for segmentation, or can be used for model selection. In particular, we discuss computation methods that apply to a Markov random field formation, implicit polynomial surface models, and parametric polynomial surface models, and present some demonstrative experiments.
Despite progress in visual servo control of robot motions, to date the corresponding motion planning problem has not been investigated. In this paper, we present an implemented planner for the special case of a polyhedral world, extending previous preimage type planners to exploit visual constraint surfaces in a fixed-camera robotic system featuring closed-loop visual servo control. We present the mathematics of a hybrid (visual/position feedback) resolved-rate motion control strategy for executing these plans, featuring projection equations defined solely in terms of a small set of observable parameters that are directly obtained from our calibration process. We conclude with experimental results, a description of ongoing research, and the contribution of our work to date.
This paper presents a probabilistic approach to segmentation that maintains a set of competing, plausible segmentation hypotheses. This is in contrast to previous approaches, in which probabilistic methods are used to converge to a single segmentation. The benefit of the approach is that belief values associated with segmentation hypotheses can be used to guide the recognition process, and the recognition process can, in turn, exert influence on the belief values associated with segmentation hypotheses in the network. In this way, segmentation and recognition can be coupled together to achieve a combination of expectation-driven segmentation and data-driven recognition. Algorithms were based on the formalism of Bayesian belief networks. By storing segmentation hypotheses in a tree structured network, the storage demands associated with maintaining the competing hypotheses can be limited. An implicit representation for segmentation hypotheses is introduced (without this implicit representation, the power set of region groupings would need to be enumerated). Likelihood measures are used both to control the expansion of the hypothesis tree and to evaluate belief in hypotheses. Local likelihood measures are used during an expansion phase, in which leaf nodes are refined into more specific hypotheses. Global likelihood measures are applied during an evaluation phase. The global likelihood measures are derived by fitting quadric surfaces to the range data. By using this expand and evaluate approach guided by a measure of entropy defined on the leaves of the tree, the application of costly numerical fitting algorithms can be limited to a small number of nodes in the tree.
Conference Committee Involvement (2)
Optomechatronic Systems Control II
2 October 2006 | Boston, Massachusetts, United States
Neural and Stochastic Methods in Image and Signal Processing II