Image understanding is a simple task for a human observer. Visual attention is automatically pointed to interesting regions by a natural objective stimulus in a first step and by prior knowledge in a second step. Saliency maps try to simulate human response and use actual eye-movements measurements as ground truth. An interesting question is: how much corruption in a digital image can affect saliency detection respect to the original image? One of the contributions of this work is to compare the performances of standard approaches with respect to different type of image corruptions and different threshold values on saliency maps. If the corruption can be estimated and/or the threshold is fixed, the results of this work can also be used to help in the selection of a method with best performance.
Why you trust in visual saliency
Bruno A;
2015-01-01
Abstract
Image understanding is a simple task for a human observer. Visual attention is automatically pointed to interesting regions by a natural objective stimulus in a first step and by prior knowledge in a second step. Saliency maps try to simulate human response and use actual eye-movements measurements as ground truth. An interesting question is: how much corruption in a digital image can affect saliency detection respect to the original image? One of the contributions of this work is to compare the performances of standard approaches with respect to different type of image corruptions and different threshold values on saliency maps. If the corruption can be estimated and/or the threshold is fixed, the results of this work can also be used to help in the selection of a method with best performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
