A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: rst forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human xations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.

We introduce a simple image descriptor referred to as the image signature. We show, within the theoretical framework of sparse signal mixing, that this quantity spatially approximates the foreground of an image. We experimentally investigate whether this approximate foreground overlaps with visually conspicuous image locations by developing a saliency algorithm based on the image signature. This saliency algorithm predicts human fixation points best among competitors on the Bruce and Tsotsos [1] benchmark data set and does so in much shorter running time. In a related experiment, we demonstrate with a change blindness data set that the distance between images induced by the image signature is closer to human perceptual distance than can be achieved using other saliency algorithms, pixel-wise, or GIST [2] descriptor methods.

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Humans adjust gaze by eye, head, and body movements. Certain stimulus properties are therefore elevated at the gaze center, but the relative contribution of eye-in-head and head-in-world movements to this selection process is unknown. Gaze- and head-centered videos recorded with a wearable device (EyeSeeCam) during free exploration are reanalyzed with respect to responses of a face-detection algorithm. In line with results on low-level features, it was found that face detections are centered near the center of gaze. By comparing environments with few and many true faces, it was inferred that actual faces are centered by eye and head movements, whereas spurious face detections ("hallucinated faces") are primarily centered by head movements alone. This analysis suggests distinct contributions to gaze allocation: head-in-world movements induce a coarse bias in the distribution of features, which eye-in-head movements refine.

-In an attempt to determine the ultimate capabilities of the Sudan/Guruswami-Sudan/Kötter-Vardy algebraic soft decision decoding algorithm for Reed-Solomon codes, we present a new method, based on the Chernoff bound, for constructing multiplicity matrices. In many cases, this technique predicts that the potential performance of ASD decoding of RS codes is significantly better than previously thought.