Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatically recovering human poses from visual input is useful but challenging due to variations in image space and the high dimensionality of the pose space. ,  assume that a human silhouette can be extracted from monocular visual input.  compare three shape descriptors that are used in the encoding of silhouettes: Fourier descriptors, shape contexts and Hu moments. An example-based approach is taken to recover upper body poses from these descriptors.  perform experiments with deformed silhouettes to test each descriptor's robustness against variations in body dimensions, viewpoint and noise. It is shown that Fourier descriptors and shape context histograms outperform Hu moments for all deformations.
['Ronald Poppe', 'Mannes Poel']
I am writing a research paper on a idea Comparison of silhouette shape descriptors for example-based human pose recovery
[{"from": "human", "value": "I am writing a research paper on a idea Comparison of silhouette shape descriptors for example-based human pose recovery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatically recovering human poses from visual input is useful but challenging due to variations in image space and the high dimensionality of the pose space. ,  assume that a human silhouette can be extracted from monocular visual input.  compare three shape descriptors that are used in the encoding of silhouettes: Fourier descriptors, shape contexts and Hu moments. An example-based approach is taken to recover upper body poses from these descriptors.  perform experiments with deformed silhouettes to test each descriptor's robustness against variations in body dimensions, viewpoint and noise. It is shown that Fourier descriptors and shape context histograms outperform Hu moments for all deformations."}]