Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

The energy backpropagation algorithm (EBP) used the net, which contains two
nodes of input layer, hidden layer and output layer. In this paper, we will use a net,
which contains three nodes and four nodes of input layer, hidden layer and output
layer. This study will compares among times of the learning, times of the
identification and times of the converging by using the three nets (2, 3 and 4 nodes)
in the Energy back propagation Algorithm. The results of experiments show the nets
which contain three nodes and four nodes have better performance for time of the
learning than the net which contains two nodes, while the net which contains two
nodes has better performance for the time of identificati