Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Computational visual perception seeks to reproduce human visionthrough the combination of visual sensors, artificial intelligence andcomputing. To this end, computer vision tasks are often reformulatedas mathematical inference problems where the objective is to determinethe set of parameters corresponding to the lost potential of a taskspecificobjective function. Graphical models have been the most popularformulation in the field over the past two decades where the problemis vied as a discrete assignment labeling one. Modularity, scalabilityand portability are the main strengths of these methods which oncecombined with efficient inference algorithms they could lead to state ofthe art results. In this tutorial  focus on the inference component ofthe problem and in particular  discuss in a systematic manner themost commonly used optimization principles in the context of graphicalmodels. Our study concerns inference over low rank models interactionsbeten variables are constrained to pairs as ll as higher orderones arbitrary set of variables determine hyper-cliques on which constraintsare introduced and seeks a concise, self-contained presentationof prior art as ll as the presentation of the current state of the artmethods in the field.
['Nikos Komodakis', 'M. Pawan Kumar', 'Nikos Paragios']
I am writing a research paper on a idea Hyper-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision
[{"from": "human", "value": "I am writing a research paper on a idea Hyper-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Computational visual perception seeks to reproduce human visionthrough the combination of visual sensors, artificial intelligence andcomputing. To this end, computer vision tasks are often reformulatedas mathematical inference problems where the objective is to determinethe set of parameters corresponding to the lost potential of a taskspecificobjective function. Graphical models have been the most popularformulation in the field over the past two decades where the problemis vied as a discrete assignment labeling one. Modularity, scalabilityand portability are the main strengths of these methods which oncecombined with efficient inference algorithms they could lead to state ofthe art results. In this tutorial  focus on the inference component ofthe problem and in particular  discuss in a systematic manner themost commonly used optimization principles in the context of graphicalmodels. Our study concerns inference over low rank models interactionsbeten variables are constrained to pairs as ll as higher orderones arbitrary set of variables determine hyper-cliques on which constraintsare introduced and seeks a concise, self-contained presentationof prior art as ll as the presentation of the current state of the artmethods in the field."}]