Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce a descriptor for shape feature extraction and matching using keypoints that are extracted from both the foreground and the background of binary images. First, distance transform (DT) is applied on the image after contour detection. Then, connected components (CCs) of pixels having the same intensity are extracted. Keypoints correspond to centers of mass of CCs. A keypoint filtering mechanism is applied by estimating the spatial stability of keypoints when successive iterations of image blurring and binarization are applied. Finally, features are extracted for each keypoint using a round layout which radius is set depending on the keypoint's location.  evaluate our descriptor using datasets of silhouette images, handwritten math expressions, and logos. Experimental results show that our descriptor is competitive compared with state-of-the-art methods, and that keypoint filtering is effective in reducing the number of keypoints without compromising matching performances.
['Houssem Chatbri', 'Kenny Davila', 'Keisuke Kameyama', 'Richard Zanibbi']
I am writing a research paper on a idea Shape matching using keypoints extracted from both the foreground and the background of binary images
[{"from": "human", "value": "I am writing a research paper on a idea Shape matching using keypoints extracted from both the foreground and the background of binary images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce a descriptor for shape feature extraction and matching using keypoints that are extracted from both the foreground and the background of binary images. First, distance transform (DT) is applied on the image after contour detection. Then, connected components (CCs) of pixels having the same intensity are extracted. Keypoints correspond to centers of mass of CCs. A keypoint filtering mechanism is applied by estimating the spatial stability of keypoints when successive iterations of image blurring and binarization are applied. Finally, features are extracted for each keypoint using a round layout which radius is set depending on the keypoint's location.  evaluate our descriptor using datasets of silhouette images, handwritten math expressions, and logos. Experimental results show that our descriptor is competitive compared with state-of-the-art methods, and that keypoint filtering is effective in reducing the number of keypoints without compromising matching performances."}]