Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Presents a method to search for stereo correspondence based on the connectivity of segments. The data structure is defined by boundary representation. The free curve can be treated by this data structure not as a straight line approximation, but as a curved line. Some candidates for correspondence pairs consist of two segments in left and right images under epipolar condition. The connectivity of two pairs is decided by their distance, intensity, and angle, based on each boundary in the left image. The similarity of paths, which are sequences of pairs, is evaluated. The main feature of our method is that it can evaluate in general situations because the similarity is calculated based on the connectivity of pairs. Multiple correspondence beten pairs is removed using this similarity, and the 3D structure is reconstructed. Applications of our work include recognition for free form objects and tracking in 3D space.
['Yoshihiro Kawai', 'Toshio Ueshiba', 'Yutaka Ishiyama', 'Yasushi Sumi', 'F. Tomitai']
I am writing a research paper on a idea Stereo correspondence using segment connectivity
[{"from": "human", "value": "I am writing a research paper on a idea Stereo correspondence using segment connectivity"}, {"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 method to search for stereo correspondence based on the connectivity of segments. The data structure is defined by boundary representation. The free curve can be treated by this data structure not as a straight line approximation, but as a curved line. Some candidates for correspondence pairs consist of two segments in left and right images under epipolar condition. The connectivity of two pairs is decided by their distance, intensity, and angle, based on each boundary in the left image. The similarity of paths, which are sequences of pairs, is evaluated. The main feature of our method is that it can evaluate in general situations because the similarity is calculated based on the connectivity of pairs. Multiple correspondence beten pairs is removed using this similarity, and the 3D structure is reconstructed. Applications of our work include recognition for free form objects and tracking in 3D space."}]