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Enhanced image saliency model based on blur identification

机译:基于模糊识别的增强图像显着性模型

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Detection of visual saliency is of great interest for a lot of computer vision applications in particular for content-based image retrieval. The work presented in this paper is devoted to develop an algorithm of saliency detection that performs adequately in predicting human fixations for stimuli containing blur and sharp regions. This work is based on an experimental study on the effect of blurriness on visual attention when observers see images with no prior knowledge in free viewing conditions. A ground-truth has been derived from this experimental study to test the saliency model we developed.
机译:视觉显着性的检测对于许多计算机视觉应用特别是基于内容的图像检索非常感兴趣。本文中介绍的工作致力于开发一种显着性检测算法,该算法在预测人体对包含模糊和锐利区域的刺激的注视方面表现良好。这项工作基于一项实验研究,即当观察者在自由观看条件下看到没有先验知识的图像时,模糊对视觉注意力的影响。从这项实验研究中得出了事实真相,以测试我们开发的显着性模型。

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