Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a shape-based algorithm for detecting and recognizing non-rigid objects from natural images. The existing literature in this domain often cannot model the objects very ll. ,  use the skeleton (medial axis) information to capture the main structure of an object, which has the particular advantage in modeling articulation and non-rigid deformation. Given a set of training samples, a tree-union structure is learned on the extracted skeletons to model the variation in configuration. Each branch on the skeleton is associated with a few part-based templates, modeling the object boundary information.  then apply sum-and-max algorithm to perform rapid object detection by matching the skeleton-based active template to the edge map extracted from a test image. The algorithm reports the detection result by a composition of the local maximum responses. Compared with the alternatives on this topic, our algorithm requires less training samples. It is simple, yet efficient and effective.  show encouraging results on two widely used benchmark image sets: the izmann horse dataset [7] and the ETHZ dataset [16].
['Xiang Bai', 'Xinggang Wang', 'Longin Jan Latecki', 'Wenyu Liu', 'Zhuowen Tu']
I am writing a research paper on a idea Active skeleton for non-rigid object detection
[{"from": "human", "value": "I am writing a research paper on a idea Active skeleton for non-rigid object detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a shape-based algorithm for detecting and recognizing non-rigid objects from natural images. The existing literature in this domain often cannot model the objects very ll. ,  use the skeleton (medial axis) information to capture the main structure of an object, which has the particular advantage in modeling articulation and non-rigid deformation. Given a set of training samples, a tree-union structure is learned on the extracted skeletons to model the variation in configuration. Each branch on the skeleton is associated with a few part-based templates, modeling the object boundary information.  then apply sum-and-max algorithm to perform rapid object detection by matching the skeleton-based active template to the edge map extracted from a test image. The algorithm reports the detection result by a composition of the local maximum responses. Compared with the alternatives on this topic, our algorithm requires less training samples. It is simple, yet efficient and effective.  show encouraging results on two widely used benchmark image sets: the izmann horse dataset [7] and the ETHZ dataset [16]."}]