Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An implementation for parametric snakes used for object tracking is proposed via generalized deterministic annealing (GDA). Given an arbitrary energy functional that quantifies the quality of the contour solution, GDA computes the snake position by approximat- ing the solution given by stochastic simulated annealing. First, the Markov chain representing the solution space for the snake position is broken into N smaller, local Markov chains representing the posi- tion of each discrete snake sample. At each annealing temperature, GDA directly approximates the stationary distribution of the local Markov chains using a mean field approximation for neighboring snake sample positions, and the final distribution reveals the solu- tion. In contrast to the typical implementation via gradient descent, annealing methods can avoid suboptimal local solutions and can be used to compute snakes that are effective in the presence of severe noise and distant initial positions. Unlike simulated annealing, GDA does not utilize random moves to slowly locate a high quality solu- tion and is thus appropriate for time critical applications. In the pa- per, synthetic experiments (on 231 images) are provided that com- pare the edge localization performance of snakes computed by GDA, simulated annealing and gradient descent for conditions of varying noise and varying initial snake position. The effectiveness of GDA is also demonstrated in a challenging real-data application (on 910 images) in which white blood cells are tracked from video mi- croscopy. © 2005 SPIE and IS&T. (DOI: 10.1117/1.1900744)
['Scott T. Acton']
I am writing a research paper on a idea Snakes for tracking via generalized deterministic annealing
[{"from": "human", "value": "I am writing a research paper on a idea Snakes for tracking via generalized deterministic annealing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An implementation for parametric snakes used for object tracking is proposed via generalized deterministic annealing (GDA). Given an arbitrary energy functional that quantifies the quality of the contour solution, GDA computes the snake position by approximat- ing the solution given by stochastic simulated annealing. First, the Markov chain representing the solution space for the snake position is broken into N smaller, local Markov chains representing the posi- tion of each discrete snake sample. At each annealing temperature, GDA directly approximates the stationary distribution of the local Markov chains using a mean field approximation for neighboring snake sample positions, and the final distribution reveals the solu- tion. In contrast to the typical implementation via gradient descent, annealing methods can avoid suboptimal local solutions and can be used to compute snakes that are effective in the presence of severe noise and distant initial positions. Unlike simulated annealing, GDA does not utilize random moves to slowly locate a high quality solu- tion and is thus appropriate for time critical applications. In the pa- per, synthetic experiments (on 231 images) are provided that com- pare the edge localization performance of snakes computed by GDA, simulated annealing and gradient descent for conditions of varying noise and varying initial snake position. The effectiveness of GDA is also demonstrated in a challenging real-data application (on 910 images) in which white blood cells are tracked from video mi- croscopy. \u00a9 2005 SPIE and IS&T. (DOI: 10.1117/1.1900744)"}]