A Hybrid Measure of Viewpoint Scoring Using Visual Perception and Information Entropy

With the rapid growing of 3Ddigital contents,there is an urgent necessity for effective retrieval and displaying of the data.Viewpoint selection via viewpoint scoring is an important approach to the problem.However,traditional viewpoint scoring either requires complex computation,or fails to provide results that conform to human visual habits.To this,we propose a hybrid viewpoint scoring measure using visual perception and information entropy.In this paper,mean curvature is regarded as a key factor for visual perception of 3Dobjects.We score the viewpoints by blending visual perception and information entropy.Viewpoints observing more important features while the features are more widely distributed will be assigned higher scores.The scoring measure is then applied in the computation of best viewpoints,while the information acquired from the viewpoints is further quantized using information theory to ensure efficient perception of the 3D objects with minimum number of viewpoints.Experimental results show that the best viewpoints obtained by the proposed method better conforms to human visual habits.At the same time,the method has a higher speed and requires no user interaction.