Cortical processing of interaural timing differences (ITDs) was investigated with event-related potential (ERP) measurements in 16 human participants who were required in separate tasks to detect or to spatially localize dichotically embedded pitches. ITDs elicited three ERP components labeled ORN, N2, and P400. The ORN occurred at a latency of 150-250 ms and was elicited by ITDs regardless of location or task. In contrast, the N2 response (250-350 ms) was strongly modulated by location and showed larger amplitudes for the localization task than for the detection task. Finally, ITDs in the detection task elicited a P400 at a latency of 400-500 ms, but this response was entirely absent from ERPs elicited by identical stimuli in the localization task. These results are consistent with a sequential model of auditory perception in which segregation of concurrent sounds is followed by domain-specific processing of object location and identity.

Abstract-We consider the task of monocular visual motion estimation from video image sequences. We hypothesise that performance on the task can be improved by incorporating an understanding of physically likely and feasible object dynamics. We test this hypothesis by incorporating a physical simulator into a least-squares estimation procedure. We initialise a full trajectory estimate using RANSAC followed by gradient descent refinement. We present results for 2D image sequences consisting of single ambiguous, visible or occluded balls, as well as results for 3D computer-generated sequences of objects in free-flight with added noise. Results suggest that restricting the estimation to allow only motions that are feasible according to the physics simulator can produce marked improvement when the observed object motion is within the limits of the physics simulator and its world model. Conversely, merely penalising deviations from feasible physical dynamics produces a consistent but incremental improvement over more common dynamics models.

Abstract-The problem of model-based object tracking in three dimensions is addressed. Most previous work on tracking assumes simple motion models, and consequently tracking typically fails in a variety of situations. Our insight is that incorporating physics models of object behaviour improves tracking performance in these cases. In particular it allows us to handle tracking in the face of rigid body interactions where there is also occlusion and fast object motion. We show how to incorporate rigid body physics simulation into a particle filter. We present two methods for this based on pose and force noise. The improvements are tested on four videos of a robot pushing an object, and results indicate that our approach performs considerably better than a plain particle filter tracker, with the force noise method producing the best results over the range of test videos.

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Özetçe -Bu bildiride, tekil görüntüden nispi derinlik tahmin etme problemini, sıralama problemi olarak ele alan ve çözüm sunan yeni bir yöntem sunuyoruz. Derinlik tahmini problemini bu şekilde yeniden formüle ederek, sıralama problemi üzerine var olan literatürden faydalanabildik ve mevcut bilgileri daha iyi sonuçlar elde etmek için uygulayabildik. Bu amaçla, sıralama literatüründen ödünç aldıgımız agırlıklandırılmış ListMLE kayıp fonksiyonunu nispi derinlik tahmini problemine kazandırdık. Ayrıca, yöntemimizin daha güçlü oldugu piksel derinligi sıralama dogrulugunu dikkate alan yeni bir metrik getirdik.Anahtar Kelimeler-derin ögrenme, derinlik tahmini, nispi derinlik tahmini, sıralamayı ögrenme.