Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Model-based recognition of an object typically involves matching dense 3D range data. The computational cost is directly affected by the amount of data of which a transformation needs to be found before carrying out the match against a model.  investigates recognition using "one-dimensional" data, more specifically, points sampled along three concurrent curves on the surface of an object. The introduced method determines the quality of match against a model in two steps. First, the Gaussian and mean curvatures at the curve intersection point are estimated and used in a table lookup to find multiple candidate points on the model that have similar local geometry. Second, starting at each point, local optimization is conducted to search for a possible location of the curve intersection on the model as ll as an orientation that leads to a good match of all data points. The best match beten the model and the data curves is chosen over the results obtained from all candidate points. The quality of this match is used for comparison against other models. Simulation and experiment have been conducted to validate the recognition approach
['Rinat Ibrayev', 'Yan-Bin Jia']
I am writing a research paper on a idea Surface Recognition by Registering Data Curves from Touch
[{"from": "human", "value": "I am writing a research paper on a idea Surface Recognition by Registering Data Curves from Touch"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Model-based recognition of an object typically involves matching dense 3D range data. The computational cost is directly affected by the amount of data of which a transformation needs to be found before carrying out the match against a model.  investigates recognition using \"one-dimensional\" data, more specifically, points sampled along three concurrent curves on the surface of an object. The introduced method determines the quality of match against a model in two steps. First, the Gaussian and mean curvatures at the curve intersection point are estimated and used in a table lookup to find multiple candidate points on the model that have similar local geometry. Second, starting at each point, local optimization is conducted to search for a possible location of the curve intersection on the model as ll as an orientation that leads to a good match of all data points. The best match beten the model and the data curves is chosen over the results obtained from all candidate points. The quality of this match is used for comparison against other models. Simulation and experiment have been conducted to validate the recognition approach"}]