Patricia Ladret

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To achieve the best image quality, noise and artifacts are generally removed at the cost of a loss of details generating the blur effect. To control and quantify the emergence of the blur effect, blur metrics have already been proposed in the literature. By associating the blur effect with the edge spreading, these metrics are sensitive not only to the(More)
Natural image categorisation and retrieval is the main challenge for image indexing. With the increase of available images and video databases, there is a real need to, first, organise the database automatically according to different semantic groups, and secondly, to take into account these large databases where most of the data is stored in a compressed(More)
An automated system that provides feedback about aesthetic quality of facial pictures could be of great interest for editing or selecting photos. Although image aesthetic quality assessment is a challenging task that requires understanding of subjective notions, the proposed work shows that facial image quality can be estimated by using low-level features(More)
This paper presents a new model of human attention that allows salient areas to be extracted from video frames. As automatic understanding of video semantic content is still far from being achieved, attention model tends to mimic the focus of the human visual system. Most existing approaches extract the saliency of images in order to be used in multiple(More)
In today’s context, where 3D content is more abundant than ever and its acceptance by the public is probably definitive, there are many discussions on controlling and improving the 3D quality. But what does this notion represent precisely? How can it be formalized and standardized? How can it be correctly evaluated? A great number of studies have(More)
In this paper, a novel bottom-up visual attention model is proposed. By using static and dynamic features, we determine salient areas in video scenes. The model is characterized by the fusion of spatial information and moving object detection. The static model, inspired by the human system, is achieved by a retinal filtering followed by a cortical(More)