Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Silhouettes are common features used by many applications in computer vision. For many of these algorithms to perform optimally, accurately segmenting the objects of interest from the background to extract the silhouettes is essential. Motion segmentation is a popular technique to segment moving objects from the background, hover such algorithms can be prone to poor segmentation, particularly in noisy or low contrast conditions. , the work of [1] combining motion detection with graph cuts, is extended into two novel implementations that aim to allow greater uncertainty in the output of the motion segmentation, providing a less restricted input to the graph cut algorithm. The proposed algorithms are evaluated on a portion of the ETISEO dataset using hand segmented ground truth data, and an improvement in performance over the motion segmentation alone and the baseline system of [1] is shown.
['Daniel Chen', 'Simon Denman', 'Clinton Fookes', 'Sridha Sridharan']
I am writing a research paper on a idea Accurate Silhouettes for Surveillance - Improved Motion Segmentation Using Graph Cuts
[{"from": "human", "value": "I am writing a research paper on a idea Accurate Silhouettes for Surveillance - Improved Motion Segmentation Using Graph Cuts"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Silhouettes are common features used by many applications in computer vision. For many of these algorithms to perform optimally, accurately segmenting the objects of interest from the background to extract the silhouettes is essential. Motion segmentation is a popular technique to segment moving objects from the background, hover such algorithms can be prone to poor segmentation, particularly in noisy or low contrast conditions. , the work of [1] combining motion detection with graph cuts, is extended into two novel implementations that aim to allow greater uncertainty in the output of the motion segmentation, providing a less restricted input to the graph cut algorithm. The proposed algorithms are evaluated on a portion of the ETISEO dataset using hand segmented ground truth data, and an improvement in performance over the motion segmentation alone and the baseline system of [1] is shown."}]