Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel method for detecting scale invariant keypoints. It fills a gap in the set of available methods, as it proposes a scale-selection mechanism for junction-type features. The method is a scale-space extension of the detector proposed by Forstner (1994) and uses the general spiral feature model of Bigun (1990) to unify different types of features within the same framework. By locally optimising the consistency of image regions with respect to the spiral model,  are able to detect and classify image structures with complementary properties over scale-space, especially star and circular shapes as interpretable and identifiable subclasses. Our motivation comes from calibrating images of structured scenes with poor texture, where blob detectors alone cannot find sufficiently many keypoints, while existing corner detectors fail due to the lack of scale invariance. The procedure can be controlled by semantically clear parameters. One obtains a set of keypoints with position, scale, type and consistency measure.  characterise the detector and show results on common benchmarks. It competes in repeatability with the Lo detector, but finds more stable keypoints in poorly textured areas, and shows comparable or higher accuracy than other recent detectors. This makes it useful for both object recognition and camera calibration.
['Wolfgang Förstner', 'Timo Dickscheid', 'Falko Schindler']
I am writing a research paper on a idea Detecting interpretable and accurate scale-invariant keypoints
[{"from": "human", "value": "I am writing a research paper on a idea Detecting interpretable and accurate scale-invariant keypoints"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel method for detecting scale invariant keypoints. It fills a gap in the set of available methods, as it proposes a scale-selection mechanism for junction-type features. The method is a scale-space extension of the detector proposed by Forstner (1994) and uses the general spiral feature model of Bigun (1990) to unify different types of features within the same framework. By locally optimising the consistency of image regions with respect to the spiral model,  are able to detect and classify image structures with complementary properties over scale-space, especially star and circular shapes as interpretable and identifiable subclasses. Our motivation comes from calibrating images of structured scenes with poor texture, where blob detectors alone cannot find sufficiently many keypoints, while existing corner detectors fail due to the lack of scale invariance. The procedure can be controlled by semantically clear parameters. One obtains a set of keypoints with position, scale, type and consistency measure.  characterise the detector and show results on common benchmarks. It competes in repeatability with the Lo detector, but finds more stable keypoints in poorly textured areas, and shows comparable or higher accuracy than other recent detectors. This makes it useful for both object recognition and camera calibration."}]