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Recovering depth information from a single still image is an important problem in computer vision. However, the problem is difficult and challenging because it has an infinite number of solutions. To address this issue, humans use numerous visual cues to infer depth. Much progress has been made towards an understanding of the visual mechanisms involved in 3D perception. Such an understanding provides...
Automatic detection of moving objects is an important task for aerial surveillance. It has been a popular and well-studied subject for the computer vision community, but is still a challenge. The method we introduce targets surveillance low-altitude mini and micro-UAVs. We take advantage of the inherent image motion on footage captured by such aerial vehicles. Our method confronts Optical Flow vectors...
The architecture of computational models of visual attention designed for videos is generally the result of a direct extension of techniques dedicated to static images. These models try to extract the salient areas of dynamic scenes (i.e. areas that may capture visual attention) by fusing static saliency maps computed frame per frame with saliency maps independently obtained from dynamic features...
The detection of salient regions in images is of great interest for a lot of computer vision applications as adaptive content delivery, smart resizing and auto-cropping, content based image retrieval or visually impaired people assistance. In this paper we focus on the effect of blurriness on human visual attention when observers see images with no prior knowledge. We investigate the hypothesis that...
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