May 23, 2012 we have (2016 pdf files in our database)
Short Desciption: ON THE ROLE OF CONTEXT IN PROBABILISTIC MODELS OF VISUAL SALIENCY Neil D. B. Bruce, Pierre Kornprobst INRIA, 2004 Route des Luci VISUAL Neil D. B. Bruce, Pierre Kornprobst INRIA, 2004 Route des Lucioles, B.P. 93, 06902 Sophia Antipolis Ce
Content Inside: ON THE ROLE OF CONTEXT IN PROBABILISTIC MODELS OF VISUAL SALIENCY Neil D. B. Bruce, Pierre Kornprobst INRIA, 2004 Route des Luci VISUAL Neil D. B. Bruce, Pierre Kornprobst INRIA, 2004 Route des Lucioles, B.P. 93, 06902 Sophia Antipolis Cedex, France ABSTRACT In recent years, many principled probabilistic definitions for the de- visual has been increased focus on the role of context in the determination visual may help in predicting the location of, or presence of features associ- ated with an object in the context of detection or recognition. Never- theless, there remains a variety of manners in which context may be exploited towards providing better judgements of salient content. In this light, we investigate the role of context in the probabilistic deter- mination of salience while presenting a number of potential avenues for future research. Index Termsmdash; saliency, context, image statistics, attention visual high-level processing on some subset of the incoming stream of vi- visual [1]. This focal processing may take the form of biased processing towards certain locations or features in the scene. At the same time, visual portant to be alerted to content that may be of interest in its own right, for example a predator suddenly appearing while an animal is searching for food. These two elements constitute respectively, the task driven top-down side of attention which serves to instigate bias towards task relevant content, and the bottom-up side which may be viewed as a stimulus driven component which results in the deploy- visual variety of models of saliency and attentional bias have emerged hav- ing as a basis a probabilistic definition for content of interest. There are a number of elements in the computation performed by these models