Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  recently introduced a novel approximation of the intractable two-dimensional hidden Markov model (2D HMM), the turbo-HMM (T-HMM), which consists of a set of interconnected horizontal and vertical 1D HMMs. ,  consider the extension of this framework to the continuous state HMM, generally referred to as the state-space model (SSM).  provide efficient approximate ansrs to the three following problems: (1) how to compute the likelihood of a set of observations; (2) how to find the sequence of states that best "explains" a set of observations; and (3) how to estimate the model parameters given a set of observations. The application of this work to the challenging problem of face recognition, in the presence of large illumination variations, illustrates the potential of our approach.
['Florent Perronnin', 'Jean-Luc Dugelay']
I am writing a research paper on a idea From turbo hidden Markov models to turbo state-space models [face recognition applications]
[{"from": "human", "value": "I am writing a research paper on a idea From turbo hidden Markov models to turbo state-space models [face recognition applications]"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  recently introduced a novel approximation of the intractable two-dimensional hidden Markov model (2D HMM), the turbo-HMM (T-HMM), which consists of a set of interconnected horizontal and vertical 1D HMMs. ,  consider the extension of this framework to the continuous state HMM, generally referred to as the state-space model (SSM).  provide efficient approximate ansrs to the three following problems: (1) how to compute the likelihood of a set of observations; (2) how to find the sequence of states that best \"explains\" a set of observations; and (3) how to estimate the model parameters given a set of observations. The application of this work to the challenging problem of face recognition, in the presence of large illumination variations, illustrates the potential of our approach."}]