Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Objects of the same class sometimes exhibit variation in shape. This shape variation has previously been modelled by means of point distribution models (PDMs) in which there is a linear relationship beten a set of shape parameters and the positions of points on the shape. A polynomial regression generalization of PDMs, which succeeds in capturing certain forms of non-linear shape variability, has also been described. Here  present a new form of PDM, which uses a multi-layer perceptron to carry out non-linear principal component analysis.  compare the performance of the new model with that of the existing models on two classes of variable shape: one exhibits bending, and the other exhibits complete rotation. The linear PDM fails on both classes of shape; the polynomial regression model succeeds for the first class of shapes but fails for the second; the new multi-layer perceptron model performs ll for both classes of shape. The new model is the most general formulation for PDMs which has been proposed to date.
['Peter D. Sozou', 'Timothy F. Cootes', 'Christopher J. Taylor', 'E. C. Di Mauro', 'Andreas Lanitis']
I am writing a research paper on a idea Non-linear point distribution modelling using a multi-layer perceptron
[{"from": "human", "value": "I am writing a research paper on a idea Non-linear point distribution modelling using a multi-layer perceptron"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Objects of the same class sometimes exhibit variation in shape. This shape variation has previously been modelled by means of point distribution models (PDMs) in which there is a linear relationship beten a set of shape parameters and the positions of points on the shape. A polynomial regression generalization of PDMs, which succeeds in capturing certain forms of non-linear shape variability, has also been described. Here  present a new form of PDM, which uses a multi-layer perceptron to carry out non-linear principal component analysis.  compare the performance of the new model with that of the existing models on two classes of variable shape: one exhibits bending, and the other exhibits complete rotation. The linear PDM fails on both classes of shape; the polynomial regression model succeeds for the first class of shapes but fails for the second; the new multi-layer perceptron model performs ll for both classes of shape. The new model is the most general formulation for PDMs which has been proposed to date."}]