A new probabilistic image segmentation model based on hypothesis testing and Gibbs Random Fields is introduced. First, a probabilistic difference measure derived from a set of hypothesis tests is introduced. Next, a GibbsMarkov Random Field model endowed with the new measure is then applied to the image segmentation problem to determine the segmented image directly through energy minimization. ...

Martensitic thin films have applications in actuators, sensors, and micromachines because of their large work output/(cycle · volume) [22]. Single-crystal martensitic thin films have recently been grown in the laboratory [12] and theoretically offer even larger work output/(cycle · volume) [5]. Bhattacharya and James [5] have rigorously derived a thin film variational principle from the three-d...

Advanced applications such as neurosurgical planning and simulation require both surface and interior anatomical information in order to be truly effective. We are developing a segmentation scheme based on collections of active surface templates embedded within an active volume. This composite system encodes high-level anatomical knowledge of both cortical surface and interior brain structures ...

This paper presents a new approach to synthesize face images under different pose changes given a single input image. The approach is based on two observations: 1. a series of face images of a single person under different poses could be mapped to a smooth manifold in the unified feature space. 2. the manifolds from different faces are separated from each other by their dissimilarities. The new...

Assuming axial symmetry, we introduce a surface of revolution approach to model the membrane at equilibrium. We consider a generating curve γ parameterized by arc length s lying in the x− z plane. The curve γ is expressed as γ(0, s1) → R γ(s) = (R(s), 0, z(s)) (S1.1) where s1 is the total arc-length. This generating curve leads to a global parametrization of the membrane expressed as X : (0, s1...

Symmetric networks designed for energy minimization such as Boltzman machines and Hop eld nets are frequently investigated for use in optimization, constraint satisfaction and approximation of NP-hard problems. Nevertheless, nding a global solution (i.e., a global minimum for the energy function) is not guaranteed and even a local solution may take an exponential number of steps. We propose an ...

Many tasks in computer vision can be formulated as energy minimization problems. In this paper, we consider a natural class of energy functions that permits discontinuities. We show that minimizing these energy functions is NP-hard. However, the energy minimization problem can be solved by computing a minimum cost multiway cut on an associated graph. This allows the use of approximation algorit...

This work is concerned with the finite element approximation of a class of stress-free martensitic microstructures modeled by multi-well energy minimization. Finite element energy-minimizing sequences are first constructed to obtain bounds on the minimum energy over all admissible finite element deformations. A series of error estimates are then derived for finite element energy minimizers.

We have designed several schemes to reduce the number of parameters in the problem of minimizing the total energy of Lennard Jones clusters We will discuss our schemes development in three dimensions The value of our work lies in the simplicity with which it provides a new approach to problems involving huge Lennard Jones clusters