Computer Vision - ECCV 2000: 6th European Conference on by Christopher M. Bishop, John M. Winn (auth.)

By Christopher M. Bishop, John M. Winn (auth.)

Ten years in the past, the inaugural ecu convention on computing device imaginative and prescient used to be held in Antibes, France. due to the fact that then, ECCV has been held biennially less than the auspices of the ecu imaginative and prescient Society at venues round Europe. This yr, the privilege of organizing ECCV 2000 falls to eire and it's a sign honour for us to host what has turn into essentially the most very important occasions within the calendar of the pc imaginative and prescient neighborhood. ECCV is a single-track convention comprising the very best quality, formerly unpublished, contributed papers on new and unique study in laptop imaginative and prescient. This 12 months, 266 papers have been submitted and, following a rigorous double-blind evaluate method, with every one paper being reviewed by means of 3 referees, 116 papers have been chosen by means of the Programme Committee for presentation on the convention. The venue for ECCV 2000 is the collage of Dublin, Trinity collage. - unded in 1592, it truly is Ireland’s oldest college and has a proud culture of scholarship within the Arts, Humanities, and Sciences, alike. The Trinity campus, set within the center of Dublin, is an oasis of tranquility and its appealing squares, dependent structures, and tree-lined enjoying- elds give you the excellent atmosphere for any conference.

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Xt−1 )}. (9) i 5 Combining Different Content Modalities So far we have not discussed in any detail what types of data can be modeled (k) by the the attributes Xt of equation (4). Because there is no constraint for these attributes to be of the same type, the Bayesian framework can naturally Learning over Multiple Temporal Scales 39 integrate many different modalities. In this work we restrict our attention to the integration of visual attributes with text annotations. Assuming a query Xt1 = {Tt1 , V1t }, composed of both text (Tt1 ) and visual attributes (V1t ), and using (9), and (4) g ∗(Xt1 ) = arg max{α log P(Vt |y = i)+α log P(Tt |y = i)+(1−α) log P(y = i|Xt−1 1 )} i (10) Disregarding the decay factor α, the comparison of this equation with (2) reveals an alternative interpretation for Bayesian integration: the optimal class is the one which would best satisfy the visual query alone but with a prior consisting of the combination of the second and third terms in the equation.

Some of those parts might correspond to an instance of the target object class (the foreground), while others stem from background clutter or are simply false detections (the background). Throughout this paper, the only information associated with an object part is its position in the image and its identity or part type. We assume that there are T different types of parts. The positions of all parts extracted from one image can be summarized in a matrix-like form,  x11 x12 , . . , x1N1  x21 x22 , .

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