Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an algorithm to generate samples from probability distributions on the space of curves.  view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm.  define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition probabilities to ensure that the samples come from the posterior distribution.  demonstrate some advantages of sampling methods such as robustness to local minima, better characterization of multi-modal distributions, access to some measures of estimation error, and ability to easily incorporate constraints on the curve.
['Ayres Fan', 'Iii. John W. Fisher', 'Iii. William M. Wells', 'James J. Levitt', 'Alan S. Willsky']
I am writing a research paper on a idea MCMC curve sampling for image segmentation
[{"from": "human", "value": "I am writing a research paper on a idea MCMC curve sampling for image segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present an algorithm to generate samples from probability distributions on the space of curves.  view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm.  define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition probabilities to ensure that the samples come from the posterior distribution.  demonstrate some advantages of sampling methods such as robustness to local minima, better characterization of multi-modal distributions, access to some measures of estimation error, and ability to easily incorporate constraints on the curve."}]