... This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights

number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light

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We present a technique for separating foreground objects from the background in a video. Our method is fast, fully au-tomatic, and makes minimal assumptions about the video. This enables handling essentially unconstrained settings, including rapidly moving background, arbitrary object mo

VISION gives you the impulse...... to make the picture your own.

Abstract — This paper proposes a novel saliency detection framework termed as saliency tree. For effective saliency mea-surement, the original image is first simplified using adaptive color quantization and region segmentation to partition the image into a set of primitive regions. Then, three

Abstract—In this paper, we propose a new method for de-tecting primary objects in unconstrained videos in a completely automatic setting. Here, we define the primary object in a video as the object that presents saliently in most of the frames. Unlike previous works only considering local saliency

and uses spatiotemporal features to regularize the segmentation. Detecting spatiotemporal T-junctions that indicate occlusion edges, we learn an occlusion edge model that is used within a colour contrast sensitive MRF to segment individual frames of a video sequence. T-junctions are learnt and classified

are necessary to locate the most interesting events in quasi real-time. We present a new theory of sensory surprise, which provides a principled and computable shortcut to important information. We develop a model that computes instantaneous low-level surprise at every location in video streams. The algorithm