Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 3D object recognition under partial object viewing is a difficult pattern recognition task. ,  introduce a neural-network solution that is robust to partial viewing of objects and noise corruption. This method directly utilizes the acquired 3D data and requires no feature extraction. The object is first parametrically represented by a continuous distance transform neural network (CDTNN) trained by the surface points of the exemplar object. The CDTNN maps any 3D coordinate into a value that corresponds to the distance beten the point and the nearest surface point of the object. Therefore, a mismatch beten the exemplar object and an unknown object can be easily computed. When encountered with deformed objects, this mismatch information can be backpropagated through the CDTNN to iteratively determine the deformation in terms of affine transform. Application to 3D heart contour delineation and invariant recognition of 3D rigid-body objects is presented.
['Yen Hao Tseng', 'Jenq Neng Hwang', 'Florence H. Sheehan']
I am writing a research paper on a idea Three-dimensional object representation and invariant recognition using continuous distance transform neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Three-dimensional object representation and invariant recognition using continuous distance transform neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 3D object recognition under partial object viewing is a difficult pattern recognition task. ,  introduce a neural-network solution that is robust to partial viewing of objects and noise corruption. This method directly utilizes the acquired 3D data and requires no feature extraction. The object is first parametrically represented by a continuous distance transform neural network (CDTNN) trained by the surface points of the exemplar object. The CDTNN maps any 3D coordinate into a value that corresponds to the distance beten the point and the nearest surface point of the object. Therefore, a mismatch beten the exemplar object and an unknown object can be easily computed. When encountered with deformed objects, this mismatch information can be backpropagated through the CDTNN to iteratively determine the deformation in terms of affine transform. Application to 3D heart contour delineation and invariant recognition of 3D rigid-body objects is presented."}]