Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces the new and porful image patch descriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order features/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian DOG approach. The obtained descriptor shows a good discriminative por when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC.
['Darshan Venkatrayappa', 'Philippe Montesinos', 'Daniel Diep', 'Baptiste Magnier']
I am writing a research paper on a idea RSD-DOG: A New Image Descriptor Based on Second Order Derivatives
[{"from": "human", "value": "I am writing a research paper on a idea RSD-DOG: A New Image Descriptor Based on Second Order Derivatives"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces the new and porful image patch descriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order features/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian DOG approach. The obtained descriptor shows a good discriminative por when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC."}]