Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Active Appearance Models (AAMs) are generative models which can describe deformable objects. Hover, the texture in basic AAMs is represented using intensity values. Despite its simplicity, this representation does not contain enough information for image matching. ,  firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor, three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments,  show that the proposed Gabor representations lead to more accurate and reliable matching beten model and images.
['Ya Su', 'Xinbo Gao', 'Dacheng Tao', 'Xuelong Li']
I am writing a research paper on a idea Gabor-based texture representation in AAMs
[{"from": "human", "value": "I am writing a research paper on a idea Gabor-based texture representation in AAMs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Active Appearance Models (AAMs) are generative models which can describe deformable objects. Hover, the texture in basic AAMs is represented using intensity values. Despite its simplicity, this representation does not contain enough information for image matching. ,  firstly propose to utilize Gabor filters to represent the image texture. The benefit of Gabor-based representation is that it can express local structures of an image. As a result, this representation can lead to more accurate matching when condition changes. Given the problem of the excessive storage and computational complexity of the Gabor, three different Gabor-based image representations are used in AAMs: (1) GaborD is the sum of Gabor filter responses over directions, (2) GaborS is the sum of Gabor filter responses over scales, and (3) GaborSD is the sum of Gabor filter responses over scales and directions. Through a large number of experiments,  show that the proposed Gabor representations lead to more accurate and reliable matching beten model and images."}]