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User-Weighted Viewpoint/Lighting Control for Multi-Object Scene
http://doi.org/10.5626/JOK.2018.45.9.888
In computer graphics, viewpoint selection for objects in a scene has been performed by evaluating the goodness of sampled viewpoints. Since the definition of a good viewpoint varies according to the user’s purpose, various measurements such as entropy and mesh saliency have been used. In this paper, we propose a method of selecting the best viewpoint and lighting for a multi-object scene, based on the user-assigned importance of each object. After sampling a viewpoint and lighting from the surrounding sphere of the scene, we render the images by combining the sampled viewpoint and lighting. We then select the best result that coincides with user-assigned importance by quantifying the saliency of each object in the rendered image. While this technique has the disadvantage of high computation cost due to the need to render combinations of viewpoints and lighting, it obtains the viewpoint and lighting most suitable for the user`s needs. In order to minimize the computation cost, an object-by-object pixel classification technique on GPU is also proposed in this paper.
Active Vision from Image-Text Multimodal System Learning
In image classification, recent CNNs compete with human performance. However, there are limitations in more general recognition. Herein we deal with indoor images that contain too much information to be directly processed and require information reduction before recognition. To reduce the amount of data processing, typically variational inference or variational Bayesian methods are suggested for object detection. However, these methods suffer from the difficulty of marginalizing over the given space. In this study, we propose an image-text integrated recognition system using active vision based on Spatial Transformer Networks. The system attempts to efficiently sample a partial region of a given image for a given language information. Our experimental results demonstrate a significant improvement over traditional approaches. We also discuss the results of qualitative analysis of sampled images, model characteristics, and its limitations.
Modeling of Visual Attention Probability for Stereoscopic Videos and 3D Effect Estimation Based on Visual Attention
Boeun Kim, Wonseok Song, Taejeong Kim
Viewers of videos are likely to absorb more information from the part of the screen that attracts visual attention. This fact has led to the visual attention models that are being used in producing and evaluating videos. In this paper, we investigate the factors that are significant to visual attention and the mathematical form of the visual attention model. We then estimated the visual attention probability using the statistical design of experiments. The analysis of variance (ANOVA) verifies that the motion velocity, distance from the screen, and amount of defocus blur affect human visual attention significantly. Using the response surface modeling (RSM), we created a visual attention score model that concerns the three factors, from which we calculate the visual attention probabilities (VAPs) of image pixels. The VAPs are directly applied to existing gradient based 3D effect perception measurement. By giving weights according to our VAPs, our algorithm achieves more accurate measurement than the existing method. The performance of the proposed measurement is assessed by comparing them with subjective evaluation as well as with existing methods. The comparison verifies that the proposed measurement outperforms the existing ones.
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