Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Two-dimensional transfer functions are an effective and ll-accepted tool in volume classification. The design of them mostly depends on the user's experience and thus remains a challenge. Therefore,  present an approach in this paper to automate the transfer function design based on 2D density plots. By exploiting their smoothness,  adopted the Morse theory to automatically decompose the feature space into a set of valley cells.  design a simplification process based on cell separability to eliminate cells which are mainly caused by noise in the original volume data. Boundary persistence is first introduced to measure the separability beten adjacent cells and to suitably merge them. Afterward, a reasonable classification result is achieved where each cell represents a potential feature in the volume data. This classification procedure is automatic and facilitates an arbitrary number and shape of features in the feature space. The opacity of each feature is determined by its persistence and size. To further incorporate the user's prior knowledge, a hierarchical feature representation is created by successive merging of the cells. With this representation, the user is allod to merge or split features of interest and set opacity and color freely. Experiments on various volumetric data sets demonstrate the effectiveness and usefulness of our approach in transfer function generation. © 2012 Wiley Periodicals, Inc.
['Yunhai Wang', 'Jian Zhang', 'Dirk J. Lehmann', 'Holger Theisel', 'Xuebin Chi']
I am writing a research paper on a idea Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots
[{"from": "human", "value": "I am writing a research paper on a idea Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Two-dimensional transfer functions are an effective and ll-accepted tool in volume classification. The design of them mostly depends on the user's experience and thus remains a challenge. Therefore,  present an approach in this paper to automate the transfer function design based on 2D density plots. By exploiting their smoothness,  adopted the Morse theory to automatically decompose the feature space into a set of valley cells.  design a simplification process based on cell separability to eliminate cells which are mainly caused by noise in the original volume data. Boundary persistence is first introduced to measure the separability beten adjacent cells and to suitably merge them. Afterward, a reasonable classification result is achieved where each cell represents a potential feature in the volume data. This classification procedure is automatic and facilitates an arbitrary number and shape of features in the feature space. The opacity of each feature is determined by its persistence and size. To further incorporate the user's prior knowledge, a hierarchical feature representation is created by successive merging of the cells. With this representation, the user is allod to merge or split features of interest and set opacity and color freely. Experiments on various volumetric data sets demonstrate the effectiveness and usefulness of our approach in transfer function generation. \u00a9 2012 Wiley Periodicals, Inc."}]