Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs.  introduce a hybrid algorithm that features log-polar mappings and nonlinear least squares optimization. The use of log-polar techniques in the spatial domain is introduced as a preprocessing module to recover large scale changes (e.g., at least four-fold) and arbitrary rotations. Although log-polar techniques are used in the Fourier-Mellin transform to accommodate rotation and scale in the frequency domain, its use in registering images subjected to very large scale changes has not yet been exploited in the spatial domain. ,  demonstrate the superior performance of the log-polar transform in featureless image registration in the spatial domain.  achieve subpixel accuracy through the use of nonlinear least squares optimization. The registration process yields the eight parameters of the perspective transformation that best aligns the two input images. Extensive testing was performed on uncalibrated real images and an array of 10,000 image pairs with known transformations derived from the Corel Stock Photo Library of royalty-free photographic images.
['Siavash Zokai', 'George Wolberg']
I am writing a research paper on a idea Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations
[{"from": "human", "value": "I am writing a research paper on a idea Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs.  introduce a hybrid algorithm that features log-polar mappings and nonlinear least squares optimization. The use of log-polar techniques in the spatial domain is introduced as a preprocessing module to recover large scale changes (e.g., at least four-fold) and arbitrary rotations. Although log-polar techniques are used in the Fourier-Mellin transform to accommodate rotation and scale in the frequency domain, its use in registering images subjected to very large scale changes has not yet been exploited in the spatial domain. ,  demonstrate the superior performance of the log-polar transform in featureless image registration in the spatial domain.  achieve subpixel accuracy through the use of nonlinear least squares optimization. The registration process yields the eight parameters of the perspective transformation that best aligns the two input images. Extensive testing was performed on uncalibrated real images and an array of 10,000 image pairs with known transformations derived from the Corel Stock Photo Library of royalty-free photographic images."}]