Tsinghua University, Beijing.
IEEE Trans Vis Comput Graph. 2013 Nov;19(11):1808-19. doi: 10.1109/TVCG.2013.99.
Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.
变化盲视是指人类无法识别图像之间的大视觉变化。在本文中，我们提出了第一个计算模型的变化盲视来量化一对图像之间的盲视程度。它包括一个新的上下文相关的显著模型和一个变化的度量，前者依赖于变化的位置，后者描述了变化的程度。特别是这个显著模型解决了背景复杂性的影响，这在变化盲视现象中起着重要作用。使用我们提出的计算模型，我们能够合成具有所需盲视程度的变化图像。用户研究和对最先进的显著模型的比较证明了我们模型的有效性。
