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Optimal parameter estimation requires simultaneous processing of all available measurements.
The complexity of this task may become too large when measurements from two or more multimodal sensor networks are avaliable.
In such cases, fusion of estimates obtained from each data set separately may be practical.
In this paper, we derive the optimal linear combination of the possibly non-linear estimators, and propose sub-optimal weightings.
We analyze the asymptotic performance gain of the first suboptimal approach with respect to the individual optimal estimates.

Affect prediction is a classical problem and has recently garnered special interest in multimedia applications. Affect prediction in movies is one such domain, potentially aiding the design as well as the impact analysis of movies.Given the large diversity in movies (such as different genres and languages), obtaining a comprehensive movie dataset for modeling affect is challenging while models trained on smaller datasets may not generalize. In this paper, we address the problem of continuous affect ratings with the availability of limited in-domain data resources.

In this paper we present the INESC Key Detection (IKD) system which incorporates a novel method for dynamically biasing key mode estimation using the spatial displacement of beat-synchronous Tonal Interval Vectors (TIVs). We evaluate the performance of the IKD system at finding the global key on three annotated audio datasets and using three key-defining profiles.

We consider the problem of inferring the hidden structure of high-dimensional
time-varying data. In particular, we aim at capturing
the dynamic relationships by representing data as valued nodes in a
sequence of graphs. Our approach is motivated by the observation
that imposing a meaningful graph topology can help solving the generally
ill-posed and challenging problem of structure inference. To
capture the temporal evolution in the sequence of graphs, we introduce
a new prior that asserts that the graph edges change smoothly

We propose a full reference stereo video quality assessment
algorithm for assessing the perceptual quality of natural stereo
videos. We exploit the separable representation of motion
and binocular disparity in the visual cortex and develop a
four stage algorithm to measure the quality of a stereoscopic
video called FLOSIM3D. First, we compute the temporal features
by utilizing an existing 2D VQA metric which measures
the temporal annoyance based on patch level statistics such
as mean, variance and minimum eigen value and pools them

Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic